Overview

The Python API (or PyAPI for short) of MLatom mainly contains 3 components: mlatom.data, mlatom.models, and mlatom.simulations.

With class mlatom.data, one can create/manipulate/save/load chemical data into objects like mlatom.data.atom, mlatom.data.molecule, and mlatom.data.molecular_database.

The class mlatom.models contains lots of computational chemistry models, based on quantum mechanics and machine learning, for making predictions on molecules. The models can be categorized into 3 types:

• methods: Models of methods that can be used ‘as is’ without the need to be trained by the user.

• ml_model: Machine learning models that require user to train them.

• model_tree_node: models consisting of other model objects (mlatom.models.methods, mlatom.models.ml_model, or another mlatom.models.model_tree_node).

mlatom.simulations makes use of an mlatom.models object to perform simulations such as geometry optimizations or molecular dynamics.

Please check a simple example that illustrates the usage of these components.

Data

!---------------------------------------------------------------------------!
! data: Module for working with data                                        !
! Implementations by: Pavlo O. Dral, Fuchun Ge,                             !
!                     Shuang Zhang, Yi-Fan Hou, Yanchi Ou, Mikołaj Martyka  !
!---------------------------------------------------------------------------!

class mlatom.data.atom(nuclear_charge: int | None = None, atomic_number: int | None = None, element_symbol: str | None = None, nuclear_mass: float | None = None, xyz_coordinates: ndarray | List | None = None)

Create an atom object.

Parameters:

• nuclear_charge (int, optional) – provide the nuclear charge to define the atom.

• atomic_number (int, optional) – provide the atomic number to define the atom.

• element_symbol (int, optional) – provide the element symbol to define the atom.

• nuclear_mass (int, optional) – provide the nuclear mass to define the atom.

• xyz_coordinates (Array-like, optional) – specify the location of atom in Cartesian coordinates.

__init__(nuclear_charge: int | None = None, atomic_number: int | None = None, element_symbol: str | None = None, nuclear_mass: float | None = None, xyz_coordinates: ndarray | List | None = None)

initialize atom object with atomic_number and xyz_coordinates only

copy(atomic_labels=None) → atom

Return a copy of the current atom object.

class mlatom.data.molecule(charge: int = 0, multiplicity: int = 1, atoms: List[atom] = None, pbc: ndarray | bool | None = None, cell: ndarray | None = None)

Create a molecule object.

Parameters:

• charge (float, optional) – Specify the charge of the molecule.

• multiplicity (int, optional) – Specify the multiplicity of the molecule.

• atoms (List[atom], optional) – Specify the atoms in the molecule.

Examples

Select an atom inside with subscription:

from mlatom.data import atom, molecule
at = atom(element_symbol = 'C')
mol = molecule(atoms = [at])
print(id(at), id(mol[0]))

id

The unique ID for this molecule.

charge

The electric charge of the molecule.

multiplicity

The multiplicity of the molecule.

load(filename: string, format: string):

Load a molecule object from a dumped file.

Updates a molecule object if initialized:

mol = molecule(); mol.load(filename='mymol.json')

Returns a molecule object if called as class method:

mol = molecule.load(filename='mymol.json')

Arguments:

filename (str): filename or path

format (str, optional): currently, only ‘json’ format is supported.

property pbc

The periodic boundary conditions of the molecule. Setting it with mol.pbc = True is equal to mol.pbc = [True, True, True].

property cell

The matrix of 3 vectors that defines the unicell. The setter of it simply wraps ase.geometry.cell.cellpar_to_cell().

property cell_coordinates: ndarray

The relative coordinates in the cell.

map_to_unicell()

Map all atoms outside the unicell into it.

read_from_xyz_file(filename: str, format: str | None = None) → molecule

Load molecular geometry from XYZ file.

Data in standard XYZ format can be read if parameter format is not specified.

Extra formats supoorted are:

• 'COLUMBUS'

• 'NEWTON-X' or 'NX'

• 'turbomol'

Parameters:

• filename (str) – The name of the file to be read.

• format (str, optional) – The format of the file.

read_from_xyz_string(string: str = None, format: str | None = None) → molecule

Load molecular geometry from XYZ string.

Data in standard XYZ format can be read if parameter format is not specified.

Extra formats supoorted are:

• 'COLUMBUS'

• 'NEWTON-X' or 'NX'

• 'turbomol'

Parameters:

• string (str) – The string input.

• format (str, optional) – The format of the string.

read_from_numpy(coordinates: ndarray, species: ndarray) → molecule

Load molecular geometry from a numpy array of coordinates and another one for species.

The shape of the input coordinates should be (N, 3), while (N,) for the input species.

Where the N is the number of atoms.

read_from_smiles_string(smi_string: str) → molecule

Generate molecular geometry from a SMILES string provided.

The geometry will be generated and optimized with Pybel’s make3D() method.

classmethod from_xyz_file(filename: str, format: str | None = None) → molecule

Classmethod wrapper for molecule.read_from_xyz_file(), returns a molecule object.

classmethod from_xyz_string(string: str = None, format: str | None = None) → molecule

Classmethod wrapper for molecule.read_from_xyz_string(), returns a molecule object.

classmethod from_numpy(coordinates: ndarray, species: ndarray) → molecule

Classmethod wrapper for molecule.read_from_numpy(), returns a molecule object.

classmethod from_smiles_string(smi_string: str) → molecule

Classmethod wrapper for molecule.read_from_smiles_string(), returns a molecule object.

add_atom_from_xyz_string(line: str) → None

Add an atom to molecule from a string in XYZ format

add_scalar_property(scalar, property_name: str = 'y') → None

Add a scalar property to the molecule. So the property can be called by molecule.<property_name>.

Parameters:

• scalar – The scalar to be added.

• property_name (str, optional) – The name assign to the scalar property.

add_xyz_derivative_property(derivative, property_name: str = 'y', xyz_derivative_property: str = 'xyz_derivatives') → None

Add a XYZ derivative property to the molecule.

Parameters:

• derivative – The derivative property to be added.

• property_name (str, optional) – The name of the associated non-derivative property.

• xyz_derivative_property (str, optional) – the name assign to the derivative property.

add_xyz_vectorial_property(vector, xyz_vectorial_property: str = 'xyz_vector') → None

Add a XYZ vectorial property to the molecule.

Parameters:

• vector – The vector to be added.

• xyz_vectorial_property (str, optional) – the name assign to the vectorial property.

write_file_with_xyz_coordinates(filename: str, format: str | None = None) → None

Write the molecular geometry data into a file. Data in standard XYZ format can be read if parameter format is not specified.

Extra formats supoorted are:

• 'COLUMBUS'

• 'NEWTON-X' or 'NX'

• 'turbomol'

Parameters:

• filename (str) – The name of the file to be written.

• format (str, optional) – The format of the file.

get_xyz_string() → str

Return the molecular geometry in a string of XYZ format.

property atomic_numbers: ndarray

The atomic numbers of the atoms in the molecule.

property element_symbols: ndarray

The element symbols of the atoms in the molecule.

property smiles: str

The SMILES representation of the molecule.

property xyz_coordinates: ndarray

The XYZ geometry of the molecule.

property kinetic_energy: float

Give out the kinetic energy (A.U.) based on the xyz_velocities.

copy(atomic_labels=None, molecular_labels=None)

Return a copy of current molecule object.

bond_length(a1, a2)

Return the distance between atom numbers a1 and a2.

Atoms are numbered from zero.

bond_angle(a1, a2, a3, degrees=True)

Return the bond angle a1-a2-a3.

The angle is defined by the vectors a1-a2 and a2-a3. Atoms are numbered from zero. based on Tom Keal MNDOtools.py, October 2007 degrees - if true, return angle in degrees, else radians.

dihedral_angle(a1, a2, a3, a4, degrees=True)

Return the dihedral angle a1-a2-a3-a4.

The angle is defined between the planes a1-a2-a3 and a2-a3-a4. Atoms are numbered from zero. based on Tom Keal MNDOtools.py, October 2007

degrees - if true, return angle in degrees, else radians.

proliferate(shifts: Iterable | None = None, XYZshifts: Iterable | None = None, Xshifts: Iterable | None = [0], Yshifts: Iterable | None = [0], Zshifts: Iterable | None = [0], PBC_constrained: bool = True) → molecule

Proliferate the unicell by specified shifts along cell vectors (called X/Y/Z here).

Returns a new molecule object.

Parameters:

• shifts (Iterable, optional) – The list of shifts to perform. Each shift should be a 3D vector that indicates the coefficient applies to the corresponding cell vector.

• XYZshifts (Iterable, optional) – Generate all possible shifts with given shift coefficients in all three directions when a list is specified. When a list of 3 lists is specified, it’s equal to setting X/Y/Zshifts

• Xshifts (Iterable, optional) – Specify all possible shift coefficients in the direction of the first cell vector.

• Yshifts (Iterable, optional) – Specify all possible shift coefficients in the direction of the second cell vector.

• Zshifts (Iterable, optional) – Specify all possible shift coefficients in the direction of the third cell vector.

• PBC_constrained (bool) – Controls whether the shifts in some directions are disabled where corresponding PBC is false. Only applies to XYZshifts.

Note

Priorities for different types of shifts:

shifts > XYZshifts > X/Y/Zshifts

Examples

Single H atom in the centre of a cubic cell (2x2x2):

mol = ml.molecule.from_numpy(np.ones((1, 3)), np.array([1]))
mol.pbc = True
mol.cell = 2

Proliferate to get two periods in all three directions, with shifts:

new_mol = mol.proliferate(
    shifts = [
        [0, 0, 0],
        [1, 0, 0],
        [0, 1, 0],
        [0, 0, 1],
        [1, 1, 0],
        [1, 0, 1],
        [0, 1, 1],
        [1, 1, 1],
    ]
)

with XYZshifts:

new_mol = mol.proliferate(XYZshifts=range(2))
# or
new_mol = mol.proliferate(XYZshifts=[range(2)]*3)

with X/Y/Zshifts:

new_mol = mol.proliferate(Xshifts=range(2), Yshifts=(0, 1), Zshifts=[0, 1]))

All scripts above will make new_mol.xyz_coordinates be:

array([[1., 1., 1.],
       [3., 1., 1.],
       [1., 3., 1.],
       [3., 3., 1.],
       [1., 1., 3.],
       [3., 1., 3.],
       [1., 3., 3.],
       [3., 3., 3.]])

dump(filename=None, format='json')

Dump the current molecule object into a file. Only in json format, which is supported now.

property state_energies: ndarray

The electronic state energies of the molecule.

property state_gradients: ndarray

The electronic state energy gradients of the molecule.

property energy_gaps: ndarray

The energy gaps of different states.

property excitation_energies: ndarray

The excitation energies of the molecule from ground state.

property nstates: np.int

The number of electronic states.

get_xyzvib_string(normal_mode=0)

Get the xyz string with geometries and displacements along the vibrational normal modes

view(normal_mode=None, slider=True, width=400, height=300)

Visualize the molecule and its vibrations if requested. Uses py3Dmol. :param normal_mode: the index of a normal mode to visualize. Default: None. :type normal_mode: integer, optional :param slider: show interactive slider to choose the mode.

Default: True (only works if normal_mode is not None).

Parameters:

• width (int, float) – the width of the window with geometry. Default 400.

• height (int, float) – the height of the window with geometry. Default 300.

class mlatom.data.molecular_database(molecules: List[molecule] = None)

Create a database for molecule objects.

Parameters:

molecules (List[molecule]) – A list of molecule to be included in the molecular database.

Examples

Select an atom inside with subscription:

from mlatom.data import atom, molecule, molecular_database
at = atom(element_symbol = 'C')
mol = molecule(atoms = [at])
molDB = molecular_database([mol])
print(id(mol) == id(molDB[0]))
# the output should be 'True'

Slicing the database like a numpy array:

from mlatom.data import molecular_database
molDB = molecular_database.from_xyz_file('devtests/al/h2_fci_db/xyz.dat')
print(len(molDB))           # 451
print(len(molDB[:100:4]))   # 25

read_from_xyz_file(filename: str, append: bool = False) → molecular_database

Load molecular geometry from XYZ file.

Parameters:

• filename (str) – The name of the file to be read.

• append (bool, optional) – Append to current database if True, otherwise clear the current database.

read_from_xyz_string(string: str, append=False) → molecular_database

Load molecular geometry from XYZ string.

Parameters:

• string (str) – The name of the file to be read.

• append (bool, optional) – Append to current database if True, otherwise clear the current database.

read_from_numpy(coordinates: ndarray, species: ndarray, append: bool = False) → molecular_database

Load molecular geometries from a numpy array of coordinates and another one for species.

The shape of the input coordinates should be (M, N, 3), while (M, N,) for the input species.

Where the N is the number of atoms and M is the number of molecules.

read_from_smiles_file(smi_file: str, append: bool = False) → molecular_database

Generate molecular geometries from a SMILES file provided.

The geometries will be generated and optimized with Pybel’s make3D() method.

read_from_smiles_string(smi_string: str, append: bool = False) → molecular_database

Generate molecular geometries from a SMILES string provided.

The geometries will be generated and optimized with Pybel’s make3D() method.

read_from_h5_file(h5_file: str = '', properties: list = None, parallel: bool | int | tuple = False, verbose: bool = False) → molecular_database

Generate molecular database from formatted h5 file. The first level should be configurations (or ensemble of molecules with same number of atoms) and the second level should be conformations and their properties. ‘species’ and ‘coordinates’ are required to construct molecule. An example format of h5 file:

` /003                  dict /003/species          array (624, 3) [int8] /003/coordinates      array (624, 3, 3) [float32] /003/energies         array (624,) [float64] /003/property1        ['wb97x/def2tzvpp'] /003/property2        array (624, 2) [int8] `

If the first two dimensions of the size of the value equals (number_of_configurations, number_of_atoms), the remaining dimension of the value will be assigned to each atom as xyz derivative properties. If the first dimensions of the size of the value equals to number of configurations, corresponging value will be assigned to each molecule. If only one value is provided for the property, it will be copied into each molecule. For example, in the above case, the properties stored in each molecule object would be: {‘energies’: float, ‘property1’:’wb97x/def2tzvpp’, ‘property2’: numpy.ndarray of size (2,0)}

Parameters:

• h5file (str) – path to h5 file.

• properties (list) – the properties to be stored in molecular database. By default all the properties presented in h5 file will be stored.

• parallel (int or tuple or bool) –

  • If int is provided, the value will be assigned to the number of workers, Batch size will be calculated automatically.

  • If tuple is provided, the first value will be assigned to the number of workers and the second value will be assigned to the batch size.

  • If bool is provided, True means all the CPUs available will be used and batch size will be adjusted accordingly.

• verbose (bool) – whether to print the loading message.

classmethod from_xyz_file(filename: str) → molecular_database

Classmethod wrapper for molecular_database.read_from_xyz_file(), returns a molecular_database object.

classmethod from_xyz_string(string: str) → molecular_database

Classmethod wrapper for molecular_database.read_from_xyz_string(), returns a molecular_database object.

classmethod from_numpy(coordinates: ndarray, species: ndarray) → molecular_database

Classmethod wrapper for molecular_database.read_from_numpy(), returns a molecular_database object.

classmethod from_smiles_file(smi_file: str) → molecular_database

Classmethod wrapper for molecular_database.read_from_smiles_file(), returns a molecular_database object.

classmethod from_smiles_string(smi_string: str | List) → molecular_database

Classmethod wrapper for molecular_database.read_from_smiles_string(), returns a molecular_database object.

add_scalar_properties(scalars, property_name: str = 'y') → None

Add scalar properties to the molecules.

Parameters:

• scalars – The scalar to be added.

• property_name (str, optional) – The name assign to the scalar property.

add_scalar_properties_from_file(filename: str, property_name: str = 'y') → None

Add scalar properties from a file to the molecules.

Parameters:

• filename (str) – Specify the text file that contains properties.

• property_name (str, optional) – The name assign to the scalar property.

add_xyz_derivative_properties(derivatives, property_name: str = 'y', xyz_derivative_property: str = 'xyz_derivatives') → None

Add a XYZ derivative property to the molecule.

Parameters:

• derivatives – The derivatives to be added.

• property_name (str, optional) – The name of the associated non-derivative property.

• xyz_derivative_property (str, optional) – the name assign to the derivative property.

add_xyz_derivative_properties_from_file(filename: str, property_name: str = 'y', xyz_derivative_property: str = 'xyz_derivatives') → None

Add a XYZ derivatives from a text file to the molecules.

Parameters:

• filename (str) – The filename that contains derivatives to be added.

• property_name (str, optional) – The name of the associated non-derivative properties.

• xyz_derivative_property (str, optional) – the name assign to the derivative properties.

add_xyz_vectorial_properties(vectors, xyz_vectorial_property: str = 'xyz_vector') → None

Add a XYZ vectorial properties to the molecules.

Parameters:

• vectors – The vectors to be added.

• xyz_vectorial_property (str, optional) – the name assign to the vectorial properties.

add_xyz_vectorial_properties_from_file(filename: str, xyz_vectorial_property: str = 'xyz_vector') → None

Add a XYZ derivatives from a text file to the molecules.

Parameters:

• filename (str) – The filename that contains vectorial properties to be added.

• xyz_vectorial_property (str, optional) – the name assign to the vectorial properties.

write_file_with_xyz_coordinates(filename: str) → None

Write the molecular geometries into a file in XYZ format.

Parameters:

filename (str) – The name of the file to be written.

get_xyz_string() → None

Return a string in XYZ format for the molecules.

write_file_with_properties(filename, property_to_write='y')

Write a property of molecules to a text file.

property atomic_numbers: ndarray

The 2D array of the atomic numbers of each atom, for all molecules in the database.

property element_symbols: ndarray

The 2D array of the element symbols of each atom, for all molecules in the database.

property ids

The IDs of the molecules in the database.

property smiles: str

The SMILES string of the molecules in the database.

write_file_with_smiles(filename)

Write the SMILES of the molecules in the database to a file.

property nuclear_masses

The nuclear_masses of the molecules in the database.

property charges

The electric charges of the molecules in the database.

property multiplicities

The multiplicities of the molecules in the database.

get_properties(property_name='y')

Return the properties of the molecules by a given property name.

set_properties(**kwargs)

Set properties of the molecules by given property name(s) as keyword(s).

get_xyz_derivative_properties(xyz_derivative_property='xyz_derivatives')

Return XYZ derivative properties by the name.

get_xyz_vectorial_properties(property_name)

Return XYZ vectorial properties by the name.

write_file_with_xyz_derivative_properties(filename, xyz_derivative_property_to_write='xyz_derivatives')

Write XYZ derivative properties into a file.

write_file_energy_gradients(filename)

Write energy gradients into a file.

write_file_with_xyz_vectorial_properties(filename, xyz_vectorial_property_to_write='xyz_vector')

Write XYZ vectorial properties into a file.

write_file_with_hessian(filename, hessian_property_to_write='hessian')

Write Hessians into a file.

append(obj)

Append a molecule/molecular database.

copy(atomic_labels=None, molecular_labels=None, molecular_database_labels=None)

Return a copy of the database.

proliferate(*args, **kwargs) → molecular_database

Proliferate the unicell by specified shifts along cell vectors.

Returns a new molecular_databse object.

Check molecule.proliferate() for details on options.

dump(filename=None, format='json')

Dump the molecular database to a file.

classmethod load(filename=None, format=None)

Load a molecular database from a file.

split(sampling='random', number_of_splits=2, split_equally=None, fraction_of_points_in_splits=None)

Splits molecular database.

Parameters:

• sampling (str, optional) – default ‘random’. Can be also ‘none’.

• split_equally (bool, optinoal) – default False; if set to True splits 50:50.

• fraction_of_points_in_splits (list, optional) – e.g., [0.8, 0.2] is the default one

• indices

property xyz_coordinates

The XYZ coordinates of each atom in every molecule.

view(width=400, height=300)

Visualize the molecular database. Uses py3Dmol.

Parameters:

• width (int, float) – the width of the window with geometry. Default 400.

• height (int, float) – the height of the window with geometry. Default 300.

class mlatom.data.molecular_trajectory(steps=None)

A class for storing/access molecular trajectory data, which is generated from a dynamics or a optimization task.

dump(filename=None, format=None)

Dump the molecular_trajectory object into a file.

Available formats are:

• 'h5md' (requires python module h5py and pyh5md)

• 'json'

• 'plain_text'

load(filename: str = None, format: str = None)

Load the previously dumped molecular_trajectory from file.

get_xyz_string() → str

Return the XYZ string of the molecules in the trajectory.

to_database() → molecular_database

Return a molecular database comprising the molecules in the trajectory.

view(width=400, height=300)

Visualize the molecular trajectory. Uses py3Dmol.

Parameters:

• width (int, float) – the width of the window with geometry. Default 400.

• height (int, float) – the height of the window with geometry. Default 300.

class mlatom.data.h5md(filename: str, data: Dict[str, Any] = {}, mode: str = 'w')

Saving trajectory data to file in H5MD format

Parameters:

• filename (str) – The filename of the h5md file output.

• data (Dict) – The data to be stored (optional, if provided, the file will be closed after storing data).

• mode (str, optional) – A string that controls the file processing mode (default value: ‘w’ for a new file, ‘r+’ for an exisiting file). The choices are listed in the table below which is consistent with pyh5md.File()

r	Readonly, file must exist
r+	Read/write, file must exist
w	Create file, truncate if exists
w- or x	Create file, fail if exists

Examples:

traj0 = h5md('traj.h5')  # open 'traj.h5'
traj1 = h5md('/tmp/test.h5', mode='r')  # open an existing file in readonly mode
traj2 = h5md('/tmp/traj2.h5', data={'time': 1.0, 'total_energy': -32.1, 'test': 8848}) # add some data to the file, then close the file

traj0.write(data) # write data to opened file
traj0(data) # an alternative way to write data

data = traj0.export() # export the data in the opened file
data = traj0() # an alternative way to export data
with h5md('test.h5') as traj: # export with a with statement
    data = traj.export()


traj0.close() # close the file

Note

the default data path in HDF5 file

particles/all:

‘box’, ‘gradients’, ‘mass’, ‘nad’, ‘names’, ‘position’, ‘species’, ‘velocities’

observables:

‘angular_momentum’, ‘generated_random_number’, ‘kinetic_energy’, ‘linear_momentum’, ‘nstatdyn’, ‘oscillator_strengths’, ‘populations’, ‘potential_energy’, ‘random_seed’, ‘sh_probabilities’, ‘total_energy’, ‘wavefunctions’,

and any other keywords

h5

the HDF5 file object

write(data: Dict[str, Any]) → None

Write data to the opened H5 file. Data should be a dictionary-like object with ‘time’ in its keys().

export() → Dict[str, ndarray]

Export the data in the opened H5 file.

Returns:

A dictionary of the trajectory data in the H5 file.

close() → None

Close the opened file.

__call__() → Dict[str, ndarray]

Export the data in the opened H5 file.

Returns:

A dictionary of the trajectory data in the H5 file.

Models

!---------------------------------------------------------------------------!
! models: Module with models                                                !
! Implementations by: Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Yuxinxin Chen,  !
!                     Peikun Zheng                                          !
!---------------------------------------------------------------------------!

class mlatom.models.model

Parent (super) class for models to enable useful features such as logging during geometry optimizations.

config_multiprocessing()

for scripts that need to be executed before running model in parallel

predict(molecular_database: molecular_database = None, molecule: molecule = None, calculate_energy: bool = False, calculate_energy_gradients: bool = False, calculate_hessian: bool = False, **kwargs)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

methods

!---------------------------------------------------------------------------!
! models: Module with models                                                !
! Implementations by: Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Yuxinxin Chen,  !
!                     Peikun Zheng                                          !
!---------------------------------------------------------------------------!

class mlatom.models.methods(method: str = None, program: str = None, **kwargs)

Create a model object with a specified method.

Parameters:

• method (str) – Specify the method. Available methods are listed in the section below.

• program (str, optional) – Specify the program to use.

• **kwargs – Other method-specific options

Available Methods:

'AIQM1', 'AIQM1@DFT', 'AIQM1@DFT*', 'AM1', 'ANI-1ccx', 'ANI-1x', 'ANI-1x-D4', 'ANI-2x', 'ANI-2x-D4', 'CCSD(T)*/CBS', 'CNDO/2', 'D4', 'DFTB0', 'DFTB2', 'DFTB3', 'GFN2-xTB', 'MINDO/3', 'MNDO', 'MNDO/H', 'MNDO/d', 'MNDO/dH', 'MNDOC', 'ODM2', 'ODM2*', 'ODM3', 'ODM3*', 'OM1', 'OM2', 'OM3', 'PM3', 'PM6', 'RM1', 'SCC-DFTB', 'SCC-DFTB-heats'.

Methods listed above can be accepted without specifying a program. The required programs still have to be installed though as described in the installation manual.

Available Programs and Their Corresponding Methods:

Program	Methods
TorchANI	'AIQM1', 'AIQM1@DFT', 'AIQM1@DFT*', 'ANI-1ccx', 'ANI-1x', 'ANI-1x-D4', 'ANI-2x', 'ANI-2x-D4', 'ANI-1xnr'
dftd4	'AIQM1', 'AIQM1@DFT', 'ANI-1x-D4', 'ANI-2x-D4', 'D4'
MNDO or Sparrow	'AIQM1', 'AIQM1@DFT', 'AIQM1@DFT*', 'MNDO', 'MNDO/d', 'ODM2*', 'ODM3*', 'OM2', 'OM3', 'PM3', 'SCC-DFTB', 'SCC-DFTB-heats'
MNDO	'CNDO/2', 'MINDO/3', 'MNDO/H', 'MNDO/dH', 'MNDOC', 'ODM2', 'ODM3', 'OM1', semiempirical OMx, DFTB, NDDO-type methods
Sparrow	'DFTB0', 'DFTB2', 'DFTB3', 'PM6', 'RM1', semiempirical OMx, DFTB, NDDO-type methods
xTB	'GFN2-xTB', semiempirical GFNx-TB methods
Orca	'CCSD(T)*/CBS', DFT
Gaussian	ab initio methods, DFT
PySCF	ab initio methods, DFT

property nthreads

int([x]) -> integer int(x, base=10) -> integer

Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.__int__(). For floating point numbers, this truncates towards zero.

If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by ‘+’ or ‘-’ and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal. >>> int(‘0b100’, base=0) 4

predict(*args, **kwargs)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

config_multiprocessing()

for scripts that need to be executed before running model in parallel

AIQM1

!---------------------------------------------------------------------------!
! aiqm1: Artificial intelligence quantum-mechanical method 1                !
! Implementations by: Peikung Zheng & Pavlo O. Dral                         !
!---------------------------------------------------------------------------!

class mlatom.aiqm1.aiqm1(method='AIQM1', qm_program=None, qm_program_kwargs={}, dftd4_kwargs={}, **kwargs)

The Artificial intelligence-quantum mechanical method as in the AIQM1 paper.

Parameters:

• method (str, optional) – AIQM method used. Currently supports AIQM1, AIQM1@DFT*, and AIQM1@DFT. Default value: AIQM1.

• qm_program (str) – The QM program used in the calculation of ODM2* part. Currently supports MNDO and Sparrow program.

• qm_program_kwargs (dictionary, optional) – Keywords passed to QM program.

# Initialize molecule
mol = ml.data.molecule()
mol.read_from_xyz_file(filename='ethanol.xyz')
# Run AIQM1 calculation
aiqm1 = ml.models.methods(method='AIQM1', qm_program='MNDO')
aiqm1.predict(molecule=mol, calculate_energy=True, calculate_energy_gradients=True)
# Get energy, gradient, and prediction uncertainty of AIQM1
energy = mol.energy
gradient = mol.gradient
std = mol.aiqm1_nn.energy_standard_deviation

predict(molecular_database=None, molecule=None, calculate_energy=True, calculate_energy_gradients=False, calculate_hessian=False, calculate_dipole_derivatives=False, nstates=1, current_state=0, **kwargs)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

class mlatom.aiqm1.atomic_energy_shift(method='AIQM1')

predict(molecular_database=None, molecule=None, calculate_energy=True, calculate_energy_gradients=False, calculate_hessian=False, nstates=1, **kwargs)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

class mlatom.aiqm1.ani_nns_in_aiqm1(method='AIQM1', model_index=0)

predict(molecular_database=None, molecule=None, calculate_energy=True, calculate_energy_gradients=False, calculate_hessian=False, nstates=1, **kwargs)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

ml_model

!---------------------------------------------------------------------------!
! models: Module with models                                                !
! Implementations by: Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Yuxinxin Chen,  !
!                     Peikun Zheng                                          !
!---------------------------------------------------------------------------!

class mlatom.models.ml_model

Useful as a superclass for the ML models that need to be trained.

train(molecular_database: molecular_database, property_to_learn: str | None = 'y', xyz_derivative_property_to_learn: str = None) → None

Train the model with molecular database provided.

Parameters:

• molecular_database (mlatom.data.molecular_database) – The database of molecules for training.

• property_to_learn (str, optional) – The label of property to be learned in model training.

• xyz_derivative_property_to_learn (str, optional) – The label of XYZ derivative property to be learned.

predict(molecular_database: data.molecular_database = None, molecule: data.molecule = None, calculate_energy: bool = False, property_to_predict: str | None = 'estimated_y', calculate_energy_gradients: bool = False, xyz_derivative_property_to_predict: str | None = 'estimated_xyz_derivatives_y', calculate_hessian: bool = False, hessian_to_predict: str | None = 'estimated_hessian_y') → None

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

• property_to_predict (str, optional) – The label name where the predicted properties to be saved.

• xyz_derivative_property_to_predict (str, optional) – The label name where the predicted XYZ derivatives to be saved.

• hessian_to_predict (str, optional) – The label name where the predicted Hessians to be saved.

generate_model_dict()

Generates model dictionary for dumping in json format.

reset()

Resets model (deletes the ML model file from the hard disk).

dump(filename=None, format='json')

Dumps model class object information in a json file (do not confused with saving the model itself, i.e., its parameters!).

calculate_validation_loss(training_kwargs=None, prediction_kwargs=None, cv_splits_molecular_databases=None, calculate_CV_split_errors=False, subtraining_molecular_database=None, validation_molecular_database=None, validation_loss_function=None, validation_loss_function_kwargs={}, debug=False)

Returns the validation loss for the given hyperparameters.

By default, the validation loss is RMSE evaluated as a geometric mean of scalar and vectorial properties, e.g., energies and gradients.

Parameters:

• training_kwargs (dict, optional) – the kwargs to be passed to yourmodel.train() function.

• prediction_kwargs (dict, optional) – the kwargs to be passed to yourmodel.predict() function.

• cv_splits_molecular_databases (list, optional) – the list with cross-validation splits, each element is molecular_database.

• calculate_CV_split_errors (bool, optional) – requests to return the errors for each cross-validation split as a list in addtion to the aggregate cross-validation error.

• subtraining_molecular_database (molecular_database, optional) – molecular database for sub-training to be passed to yourmodel.train() function.

• validation_molecular_database (molecular_database, optional) – molecular database for validation to be passed to yourmodel.predict() function.

• validation_loss_function (function, optional) – user-defined validation function.

• validation_loss_function_kwargs (dict, optional) – kwargs for above validation_loss_function.

optimize_hyperparameters(hyperparameters=None, training_kwargs=None, prediction_kwargs=None, cv_splits_molecular_databases=None, subtraining_molecular_database=None, validation_molecular_database=None, optimization_algorithm=None, optimization_algorithm_kwargs={}, maximum_evaluations=10000, validation_loss_function=None, validation_loss_function_kwargs={}, debug=False)

Optimizes hyperparameters by minimizing the validation loss.

By default, the validation loss is RMSE evaluated as a geometric mean of scalar and vectorial properties, e.g., energies and gradients.

Parameters:

• hyperparameters (list, required) – the list with strings - names of hyperparameters. Hyperparameters themselves must be in youmodel.hyperparameters defined with class instance hyperparameters consisting of hyperparameter defining the optimization space.

• training_kwargs (dict, optional) – the kwargs to be passed to yourmodel.train() function.

• prediction_kwargs (dict, optional) – the kwargs to be passed to yourmodel.predict() function.

• cv_splits_molecular_databases (list, optional) – the list with cross-validation splits, each element is molecular_database.

• calculate_CV_split_errors (bool, optional) – requests to return the errors for each cross-validation split as a list in addtion to the aggregate cross-validation error.

• subtraining_molecular_database (molecular_database, optional) – molecular database for sub-training to be passed to yourmodel.train() function.

• validation_molecular_database (molecular_database, optional) – molecular database for validation to be passed to yourmodel.predict() function.

• validation_loss_function (function, optional) – user-defined validation function.

• validation_loss_function_kwargs (dict, optional) – kwargs for above validation_loss_function.

• optimization_algorithm (str, required) – optimization algorithm. No default, must be specified among: ‘grid’ (‘brute’), ‘TPE’, ‘Nelder-Mead’, ‘BFGS’, ‘L-BFGS-B’, ‘Powell’, ‘CG’, ‘Newton-CG’, ‘TNC’, ‘COBYLA’, ‘SLSQP’, ‘trust-constr’, ‘dogleg’, ‘trust-krylov’, ‘trust-exact’.

• optimization_algorithm_kwargs (dict, optional) – kwargs to be passed to optimization algorithm, e.g., {'grid_size': 5} (default 9 for the grid search).

• maximum_evaluations (int, optional) – maximum number of optimization evaluations (default: 10000) supported by all optimizers except for grid search.

Saves the final hyperparameters in yourmodel.hyperparameters adn validation loss in yourmodel.validation_loss.

class mlatom.models.hyperparameter(value: Any = None, optimization_space: str = 'linear', dtype: Callable | None = None, name: str = '', minval: Any = None, maxval: Any = None, step: Any = None, choices: Iterable[Any] = [], **kwargs)

Class of hyperparameter object, containing data could be used in hyperparameter optimizations.

Parameters:

• value (Any, optional) – The value of the hyperparameter.

• optimization_space (str, optional) – Defines the space for hyperparameter. Currently supports 'linear', and 'log'.

• dtype (Callable, optional) – A callable object that forces the data type of value. Automatically choose one if set to None.

update(new_hyperparameter: hyperparameter) → None

Update hyperparameter with data in another instance.

Parameters:

new_hyperparameter (mlatom.models.hyperparamters) – Whose data are to be applied to the current instance.

copy()

Returns a copy of current instance.

Returns:

a new instance copied from current one.

Return type:

mlatom.models.hyperparamter

class mlatom.models.hyperparameters(dict=None, /, **kwargs)

Class for storing hyperparameters, values are auto-converted to mlatom.models.hyperparameter objects. Inherit from collections.UserDict.

Initiaion:

Initiate with a dictinoary or kwargs or both.

e.g.:

hyperparamters({'a': 1.0}, b=hyperparameter(value=2, minval=0, maxval=4))

copy(keys: Iterable[str] | None = None) → hyperparameters

Returns a copy of current instance.

Parameters:

keys (Iterable[str], optional) – If keys provided, only the hyperparameters selected by keys will be copied, instead of all hyperparameters.

Returns:

a new instance copied from current one.

Return type:

mlatom.models.hyperparamters

class mlatom.models.kreg(model_file: str | None = None, ml_program: str = 'KREG_API', equilibrium_molecule: molecule | None = None, prior: float = 0, nthreads: int | None = None, hyperparameters: Dict[str, Any] | hyperparameters = {})

Create a KREG model object.

Parameters:

• model_file (str, optional) – The name of the file where the model should be dumped to or loaded from.

• ml_program (str, optional) – Specify which ML program to use. Avaliable options: 'KREG_API', 'MLatomF.

• equilibrium_molecule (mlatom.data.molecule | None) – Specify the equilibrium geometry to be used to generate RE descriptor. The geometry with lowest energy/value will be selected if set to None.

• prior (default - None) – the prior can be ‘mean’, None (0.0), or any floating point number.

• hyperparameters (Dict[str, Any] | mlatom.models.hyperparameters, optional) – Updates the hyperparameters of the model with provided.

generate_model_dict()

Generates model dictionary for dumping in json format.

train(molecular_database=None, property_to_learn=None, xyz_derivative_property_to_learn=None, save_model=True, invert_matrix=False, matrix_decomposition=None, prior=None, hyperparameters: Dict[str, Any] | hyperparameters = {})

Train the KREG model with molecular database provided.

Parameters:

• molecular_database (mlatom.data.molecular_database) – The database of molecules for training.

• property_to_learn (str, optional) – The label of property to be learned in model training.

• xyz_derivative_property_to_learn (str, optional) – The label of XYZ derivative property to be learned.

• prior (str or float or int, optional) – default zero prior. It can also be ‘mean’ and any user-defined number.

predict(molecular_database=None, molecule=None, calculate_energy=False, calculate_energy_gradients=False, calculate_hessian=False, property_to_predict=None, xyz_derivative_property_to_predict=None, hessian_to_predict=None)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

• property_to_predict (str, optional) – The label name where the predicted properties to be saved.

• xyz_derivative_property_to_predict (str, optional) – The label name where the predicted XYZ derivatives to be saved.

• hessian_to_predict (str, optional) – The label name where the predicted Hessians to be saved.

mlatom.models.ani(**kwargs)

Returns an ANI model object (see mlatom.interfaces.torchani_interface.ani).

mlatom.models.dpmd(**kwargs)

Returns a DPMD model object (see mlatom.interfaces.dpmd_interface.dpmd).

mlatom.models.gap(**kwargs)

Returns a GAP model object (see mlatom.interfaces.gap_interface.gap).

mlatom.models.physnet(**kwargs)

Returns a PhysNet model object (see mlatom.interfaces.physnet_interface.physnet).

mlatom.models.sgdml(**kwargs)

Returns an sGDML model object (see mlatom.interfaces.sgdml_interface.sgdml).

mlatom.models.mace(**kwargs)

Returns an MACE model object (see mlatom.interfaces.mace_interface.mace).

model_tree_node

!---------------------------------------------------------------------------!
! models: Module with models                                                !
! Implementations by: Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Yuxinxin Chen,  !
!                     Peikun Zheng                                          !
!---------------------------------------------------------------------------!

class mlatom.models.model_tree_node(name=None, parent=None, children=None, operator=None, model=None)

Create a model tree node.

Parameters:

• name (str) – The name assign to the object.

• parent – The parent of the model node.

• children – The children of this model tree node.

• operator – Specify the operation to be made when making predictions.

predict(**kwargs)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

dump(filename=None, format='json')

Dump the object to a file.

Interfaces

Interfaces to third-party software.

TorchANI

!---------------------------------------------------------------------------!
! Interface_TorchANI: Interface between TorchANI and MLatom                 !
! Implementations by: Fuchun Ge and Max Pinheiro Jr                         !
! Defines the MS-ANI model                                                  !
! Implemented by: Mikolaj Martyka                                           !
!---------------------------------------------------------------------------!

class mlatom.interfaces.torchani_interface.StateInputNet(*args: Any, **kwargs: Any)

class mlatom.interfaces.torchani_interface.ani(model_file: str = None, device: str = None, hyperparameters: Dict[str, Any] | hyperparameters = {}, verbose=1, **kwargs)

Create an ANI (ANAKIN-ME: Accurate NeurAl networK engINe for Molecular Energie) model object.

Interfaces to TorchANI.

Parameters:

• model_file (str, optional) – The filename that the model to be saved with or loaded from.

• device (str, optional) – Indicate which device the calculation will be run on. i.e. ‘cpu’ for CPU, ‘cuda’ for Nvidia GPUs. When not speficied, it will try to use CUDA if there exists valid CUDA_VISIBLE_DEVICES in the environ of system.

• hyperparameters (Dict[str, Any] | mlatom.models.hyperparameters, optional) – Updates the hyperparameters of the model with provided.

• verbose (int, optional) – 0 for silence, 1 for verbosity.

reset()

Resets model (deletes the ML model file from the hard disk).

save(model_file: str = '') → None

Save the model to file (.pt format).

Parameters:

model_file (str, optional) – The filename that the model to be saved into. If not provided, a randomly generated string will be used.

load(model_file: str = '', species_order: List[str] | None = None, AEV_parameters: Dict | None = None, self_energies: List[float] | None = None, reset_parameters: bool = False, method: str = '') → None

Load a saved ANI model from file (.pt format).

Parameters:

• model_file (str) – The filename of the model to be loaded.

• species_order (List[str], optional) – Manually provide species order if it’s not present in the saved model.

• AEV_parameters (Dict, optional) – Manually provide AEV parameters if it’s not present in the saved model.

• self_energies (List[float], optional) – Manually provide self energies if it’s not present in the saved model.

• reset_parameters (bool) – Reset network paramters in the loaded model.

• method (str) – Load an ANI method, see also ani.load_ani_model().

load_ani_model(method: str, **hyperparameters) → None

Load an ANI model.

Arguments:

method(str): Can be 'ANI-1x', 'ANI-1ccx', or 'ANI-2x'.

train(molecular_database: data.molecular_database, property_to_learn: str = 'energy', xyz_derivative_property_to_learn: str = None, validation_molecular_database: data.molecular_database | str | None = 'sample_from_molecular_database', hyperparameters: Dict[str, Any] | models.hyperparameters = {}, spliting_ratio: float = 0.8, save_model: bool = True, file_to_save_model: str = None, check_point: str = None, reset_optim_state: bool = False, use_last_model: bool = False, reset_parameters: bool = False, reset_network: bool = False, reset_optimizer: bool = False, reset_energy_shifter: bool | torchani.utils.EnergyShifter | list = False, save_every_epoch: bool = False, save_epoch_interval: int = None, energy_weighting_function: Callable = None, energy_weighting_function_kwargs: dict = {}, verbose: int = None) → None

Train the model with molecular database provided.

Parameters:

• molecular_database (mlatom.data.molecular_database) – The database of molecules for training.

• property_to_learn (str, optional) – The label of property to be learned in model training.

• xyz_derivative_property_to_learn (str, optional) – The label of XYZ derivative property to be learned.

• validation_molecular_database (mlatom.data.molecular_database | str, optional) – Explicitly defines the database for validation, or use 'sample_from_molecular_database' to make it sampled from the training set.

• hyperparameters (Dict[str, Any] | mlatom.models.hyperparameters, optional) – Updates the hyperparameters of the model with provided.

• spliting_ratio (float, optional) – The ratio sub-training dataset in the whole training dataset.

• save_model (bool, optional) – Whether save the model to disk during training process. Note that the model might be saved many times during training.

• reset_optim_state (bool, optional) – Whether to reset the state of optimizer.

• use_last_model (bool, optional) – Whether to keep the self.model as it is in the last training epoch. If False, the best model will be loaded to memory at the end of training.

• reset_parameters (bool, optional) – Whether to reset the model’s parameters before training.

• reset_network (bool, optional) – Whether to re-construct the network before training.

• reset_optimizer (bool, optional) – Whether to re-define the optimizer before training .

• save_every_epoch (bool, optional) – Whether to save model in every epoch, valid when save_model is True.

• save_epoch_interval (int, optional) – The interval to save each epoch.

• energy_weighting_function (Callable, optional) – A weighting function \(\mathit{W}(\mathbf{E_ref})\) that assign weights to training points based on their reference energies.

• energy_weighting_function_kwargs (dict, optional) – Extra weighting function arguments in a dictionary.

predict(molecular_database: data.molecular_database = None, molecule: data.molecule = None, calculate_energy: bool = False, calculate_energy_gradients: bool = False, calculate_hessian: bool = False, property_to_predict: str | None = 'estimated_y', xyz_derivative_property_to_predict: str | None = None, hessian_to_predict: str | None = None, batch_size: int = 65536) → None

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

• property_to_predict (str, optional) – The label name where the predicted properties to be saved.

• xyz_derivative_property_to_predict (str, optional) – The label name where the predicted XYZ derivatives to be saved.

• hessian_to_predict (str, optional) – The label name where the predicted Hessians to be saved.

• batch_size (int, optional) – The batch size for batch-predictions.

NN_initialize(a: float = 1.0) → None

Reset the network parameters using torch.nn.init.kaiming_normal_().

Parameters:

a (float) – Check torch.nn.init.kaiming_normal_().

fix_layers(layers_to_fix: List[List[int]] | List[int])

Fix specific layers to be non-trainable for each element.

Parameters:

layers_to_fix (List) –

Should be:

• A list of integers. Layers indicated by the integers will be fixed.

• A list of lists of integers. Each sub-list defines the layers to be fixed for each species, in the order of self.species_order.

Examples

import mlatom as ml
>>> ani = ml.models.ani(model_file='ANI.pt')
model loade from ANI.pt
>>> ani.model # show model summary
Sequential(
(0): AEVComputer()
(1): ANIModel(
    (C): Sequential(
    (0): Linear(in_features=240, out_features=160, bias=True)
    (1): CELU(alpha=0.1)
    (2): Linear(in_features=160, out_features=128, bias=True)
    (3): CELU(alpha=0.1)
    (4): Linear(in_features=128, out_features=96, bias=True)
    (5): CELU(alpha=0.1)
    (6): Linear(in_features=96, out_features=1, bias=True)
    )
    (H): Sequential(
    (0): Linear(in_features=240, out_features=160, bias=True)
    (1): CELU(alpha=0.1)
    (2): Linear(in_features=160, out_features=128, bias=True)
    (3): CELU(alpha=0.1)
    (4): Linear(in_features=128, out_features=96, bias=True)
    (5): CELU(alpha=0.1)
    (6): Linear(in_features=96, out_features=1, bias=True)
    )
    (O): Sequential(
    (0): Linear(in_features=240, out_features=160, bias=True)
    (1): CELU(alpha=0.1)
    (2): Linear(in_features=160, out_features=128, bias=True)
    (3): CELU(alpha=0.1)
    (4): Linear(in_features=128, out_features=96, bias=True)
    (5): CELU(alpha=0.1)
    (6): Linear(in_features=96, out_features=1, bias=True)
    )
  )
)
>>> parameters = dict(a.model.named_parameters())
>>> {key: parameters[key].requires_grad for key in parameters} # show the trainability of parameters
{'1.C.0.weight': True, '1.C.0.bias': True, '1.C.2.weight': True, '1.C.2.bias': True, '1.C.4.weight': True, '1.C.4.bias': True, '1.C.6.weight': True, '1.C.6.bias': True, '1.H.0.weight': True, '1.H.0.bias': True, '1.H.2.weight': True, '1.H.2.bias': True, '1.H.4.weight': True, '1.H.4.bias': True, '1.H.6.weight': True, '1.H.6.bias': True, '1.O.0.weight': True, '1.O.0.bias': True, '1.O.2.weight': True, '1.O.2.bias': True, '1.O.4.weight': True, '1.O.4.bias': True, '1.O.6.weight': True, '1.O.6.bias': True}
>>> ani.fix_layers([[0, 2], [0], [4]]) # fix layer 0 and 2 for C, layer 0 for H, and layer 4 for O
>>> {key: parameters[key].requires_grad for key in parameters}
{'1.C.0.weight': False, '1.C.0.bias': False, '1.C.2.weight': False, '1.C.2.bias': False, '1.C.4.weight': True, '1.C.4.bias': True, '1.C.6.weight': True, '1.C.6.bias': True, '1.H.0.weight': False, '1.H.0.bias': False, '1.H.2.weight': True, '1.H.2.bias': True, '1.H.4.weight': True, '1.H.4.bias': True, '1.H.6.weight': True, '1.H.6.bias': True, '1.O.0.weight': True, '1.O.0.bias': True, '1.O.2.weight': True, '1.O.2.bias': True, '1.O.4.weight': False, '1.O.4.bias': False, '1.O.6.weight': True, '1.O.6.bias': True}
>>> ani.fix_layers([[0, 2, 4]]) # fix layer 0, 2, and 4 for all networks
{'1.C.0.weight': False, '1.C.0.bias': False, '1.C.2.weight': False, '1.C.2.bias': False, '1.C.4.weight': False, '1.C.4.bias': False, '1.C.6.weight': True, '1.C.6.bias': True, '1.H.0.weight': False, '1.H.0.bias': False, '1.H.2.weight': False, '1.H.2.bias': False, '1.H.4.weight': False, '1.H.4.bias': False, '1.H.6.weight': True, '1.H.6.bias': True, '1.O.0.weight': False, '1.O.0.bias': False, '1.O.2.weight': False, '1.O.2.bias': False, '1.O.4.weight': False, '1.O.4.bias': False, '1.O.6.weight': True, '1.O.6.bias': True}

class mlatom.interfaces.torchani_interface.msani(model_file: str = None, device: str = None, hyperparameters: Dict[str, Any] | hyperparameters = {}, verbose=1, nstates=1, validate_train=True)

Create an `MS-ANI’, an extension of the ANI NN model for multi-state learning. First described in <10.26434/chemrxiv-2024-dtc1w>.

Interfaces to TorchANI.

Parameters:

• model_file (str, optional) – The filename that the model to be saved with or loaded from.

• device (str, optional) – Indicate which device the calculation will be run on. i.e. ‘cpu’ for CPU, ‘cuda’ for Nvidia GPUs. When not speficied, it will try to use CUDA if there exists valid CUDA_VISIBLE_DEVICES in the environ of system.

• hyperparameters (Dict[str, Any] | mlatom.models.hyperparameters, optional) – Updates the hyperparameters of the model with provided.

• verbose (int, optional) – 0 for silence, 1 for verbosity.

reset()

Resets model (deletes the ML model file from the hard disk).

save(model_file: str = '') → None

Save the model to file (.pt format).

Parameters:

model_file (str, optional) – The filename that the model to be saved into. If not provided, a randomly generated string will be used.

load(model_file: str = '', species_order: List[str] | None = None, AEV_parameters: Dict | None = None, self_energies: List[float] | None = None, reset_parameters: bool = False, method: str = '') → None

Load a saved ANI model from file (.pt format).

Parameters:

• model_file (str) – The filename of the model to be loaded.

• species_order (List[str], optional) – Manually provide species order if it’s not present in the saved model.

• AEV_parameters (Dict, optional) – Manually provide AEV parameters if it’s not present in the saved model.

• self_energies (List[float], optional) – Manually provide self energies if it’s not present in the saved model.

• reset_parameters (bool) – Reset network paramters in the loaded model.

• method (str) – Load an ANI method, see also ani.load_ani_model().

load_ani_model(method: str, **hyperparameters) → None

Load an ANI model.

Arguments:

method(str): Can be 'ANI-1x', 'ANI-1ccx', or 'ANI-2x'.

train(molecular_database: data.molecular_database, property_to_learn: str = 'energy', xyz_derivative_property_to_learn: str = None, validation_molecular_database: data.molecular_database | str | None = 'sample_from_molecular_database', hyperparameters: Dict[str, Any] | models.hyperparameters = {}, spliting_ratio: float = 0.8, save_model: bool = True, check_point: str = None, reset_optim_state: bool = False, use_last_model: bool = False, reset_parameters: bool = False, reset_network: bool = False, reset_optimizer: bool = False, save_every_epoch: bool = False, energy_weighting_function: Callable = None, energy_weighting_function_kwargs: dict = {}) → None

Train the model with molecular database provided.

Parameters:

• molecular_database (mlatom.data.molecular_database) – The database of molecules for training.

• property_to_learn (str, optional) – The label of property to be learned in model training.

• xyz_derivative_property_to_learn (str, optional) – The label of XYZ derivative property to be learned.

• validation_molecular_database (mlatom.data.molecular_database | str, optional) – Explicitly defines the database for validation, or use 'sample_from_molecular_database' to make it sampled from the training set.

• hyperparameters (Dict[str, Any] | mlatom.models.hyperparameters, optional) – Updates the hyperparameters of the model with provided.

• spliting_ratio (float, optional) – The ratio sub-training dataset in the whole training dataset.

• save_model (bool, optional) – Whether save the model to disk during training process. Note that the model might be saved many times during training.

• reset_optim_state (bool, optional) – Whether to reset the state of optimizer.

• use_last_model (bool, optional) – Whether to keep the self.model as it is in the last training epoch. If False, the best model will be loaded to memory at the end of training.

• reset_parameters (bool, optional) – Whether to reset the model’s parameters before training.

• reset_network (bool, optional) – Whether to re-construct the network before training.

• reset_optimizer (bool, optional) – Whether to re-define the optimizer before training .

• save_every_epoch (bool, optional) – Whether to save model in every epoch, valid when save_model is True.

• energy_weighting_function (Callable[Array-like], optional) – A weighting function \(\mathit{W}(\mathbf{E_ref})\) that assign weights to training points based on their reference energies.

predict(molecular_database: data.molecular_database = None, molecule: data.molecule = None, nstates=1, current_state=0, calculate_energy: bool = False, calculate_energy_gradients: bool = False, calculate_hessian: bool = False, property_to_predict: str | None = 'estimated_y', xyz_derivative_property_to_predict: str | None = None, hessian_to_predict: str | None = None, batch_size: int = 65536) → None

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

• property_to_predict (str, optional) – The label name where the predicted properties to be saved.

• xyz_derivative_property_to_predict (str, optional) – The label name where the predicted XYZ derivatives to be saved.

• hessian_to_predict (str, optional) – The label name where the predicted Hessians to be saved.

• batch_size (int, optional) – The batch size for batch-predictions.

NN_initialize(a: float = 1.0) → None

Reset the network parameters using torch.nn.init.kaiming_normal_().

Parameters:

a (float) –

Check torch.nn.init.kaiming_normal_().

fix_layers(layers_to_fix: List[List[int]] | List[int])

Fix specific layers to be non-trainable for each element.

Parameters:

layers_to_fix (List) –

Should be:

• A list of integers. Layers indicate by the integers will be fixed

• A list of lists of integers. Each sub-list defines the layers to be fixed for each species, in the order of self.species_order.

class mlatom.interfaces.torchani_interface.ani_child(parent, index, name='ani_child', device='cpu')

predict(molecular_database: molecular_database = None, molecule: molecule = None, calculate_energy: bool = False, calculate_energy_gradients: bool = False, calculate_hessian: bool = False, batch_size: int = 65536) → None

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

class mlatom.interfaces.torchani_interface.torchani_methods(method: str = 'ANI-1ccx', device: str = 'cpu', **kwargs)

Create a model object with one of the ANI methods

Parameters:

• method (str) – A string that specifies the method. Available choices: 'ANI-1x', 'ANI-1ccx', 'ANI-2x', 'ANI-1ccx-gelu' or 'ANI-1x-gelu'.

• device (str, optional) – Indicate which device the calculation will be run on, i.e. ‘cpu’ for CPU, ‘cuda’ for Nvidia GPUs. When not speficied, it will try to use CUDA if there exists valid CUDA_VISIBLE_DEVICES in the environ of system.

predict(molecular_database: data.molecular_database = None, molecule: data.molecule = None, calculate_energy: bool = False, calculate_energy_gradients: bool = False, calculate_hessian: bool = False, batch_size: int = 65536) → None

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

• batch_size (int, optional) – The batch size for batch-predictions.

class mlatom.interfaces.torchani_interface.aimnet2_methods(method: str = 'AIMNet2@b973c', device: str = 'cpu', **kwargs)

Universal ML methods with AIMNet2: https://doi.org/10.26434/chemrxiv-2023-296ch

Parameters:

• method (str) – A string that specifies the method. Available choices: 'AIMNet2@b973c' and 'AIMNet2@wb97m-d3'.

• device (str, optional) – Indicate which device the calculation will be run on, i.e. ‘cpu’ for CPU, ‘cuda’ for Nvidia GPUs. When not speficied, it will try to use CUDA if there exists valid CUDA_VISIBLE_DEVICES in the environ of system.

predict(molecular_database: data.molecular_database = None, molecule: data.molecule = None, calculate_energy: bool = False, calculate_energy_gradients: bool = False, calculate_hessian: bool = False, batch_size: int = 65536) → None

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

DeepMD-kit

!---------------------------------------------------------------------------!
! Interface_DeePMDkit: Interface between DeePMD-kit and MLatom              !
! Implementations by: Fuchun Ge                                             !
!---------------------------------------------------------------------------!

class mlatom.interfaces.dpmd_interface.dpmd(model_file=None, hyperparameters={}, verbose=1)

Create an DeepPot-SE model object.

Interfaces to DeepMD-kit.

Parameters:

• model_file (str, optional) – The filename that the model to be saved with or loaded from.

• hyperparameters (Dict[str, Any] | mlatom.models.hyperparameters, optional) – Updates the hyperparameters of the model with provided.

• verbose (int, optional) – 0 for silence, 1 for verbosity.

train(molecular_database: molecular_database, property_to_learn: str = 'energy', xyz_derivative_property_to_learn: str = None, validation_molecular_database: molecular_database | str | None = 'sample_from_molecular_database', hyperparameters: Dict[str, Any] | hyperparameters = {}, spliting_ratio=0.8, stdout=None, stderr=None, json_input=None, dirname=None)

Train the model with molecular database provided.

Parameters:

• molecular_database (mlatom.data.molecular_database) – The database of molecules for training.

• property_to_learn (str, optional) – The label of property to be learned in model training.

• xyz_derivative_property_to_learn (str, optional) – The label of XYZ derivative property to be learned.

predict(molecular_database: data.molecular_database = None, molecule: data.molecule = None, calculate_energy: bool = False, calculate_energy_gradients: bool = False, calculate_hessian: bool = False, property_to_predict: str | None = 'estimated_y', xyz_derivative_property_to_predict: str | None = None, hessian_to_predict: str | None = None) → None

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

• property_to_predict (str, optional) – The label name where the predicted properties to be saved.

• xyz_derivative_property_to_predict (str, optional) – The label name where the predicted XYZ derivatives to be saved.

• hessian_to_predict (str, optional) – The label name where the predicted Hessians to be saved.

GAP/QUIP

!---------------------------------------------------------------------------!
! Interface_GAP: Interface between GAP and MLatom                           !
! Implementations by: Fuchun Ge                                             !
!---------------------------------------------------------------------------!

class mlatom.interfaces.gap_interface.gap(model_file=None, hyperparameters={}, verbose=0)

Create an GAP-SOAP model object.

Interfaces to QUIP.

Parameters:

• model_file (str, optional) – The filename that the model to be saved with or loaded from.

• hyperparameters (Dict[str, Any] | mlatom.models.hyperparameters, optional) – Updates the hyperparameters of the model with provided.

• verbose (int, optional) – 0 for silence, 1 for verbosity.

train(molecular_database: molecular_database, property_to_learn: str = 'energy', xyz_derivative_property_to_learn: str = None, hyperparameters: Dict[str, Any] | hyperparameters = {}, stdout=None, stderr=None)

Train the model with molecular database provided.

Parameters:

• molecular_database (mlatom.data.molecular_database) – The database of molecules for training.

• property_to_learn (str, optional) – The label of property to be learned in model training.

• xyz_derivative_property_to_learn (str, optional) – The label of XYZ derivative property to be learned.

predict(molecular_database: data.molecular_database = None, molecule: data.molecule = None, calculate_energy: bool = False, calculate_energy_gradients: bool = False, calculate_hessian: bool = False, property_to_predict: str | None = 'estimated_y', xyz_derivative_property_to_predict: str | None = None, hessian_to_predict: str | None = None) → None

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

• property_to_predict (str, optional) – The label name where the predicted properties to be saved.

• xyz_derivative_property_to_predict (str, optional) – The label name where the predicted XYZ derivatives to be saved.

• hessian_to_predict (str, optional) – The label name where the predicted Hessians to be saved.

PhysNet

!---------------------------------------------------------------------------!
! Interface_PhysNet: Interface between PhysNet and MLatom                   !
! Implementations by: Fuchun Ge and Max Pinheiro Jr                         !
!---------------------------------------------------------------------------!

class mlatom.interfaces.physnet_interface.physnet(model_file=None, hyperparameters={}, verbose=1)

Create an PhysNet model object.

Interfaces to PhysNet program.

Parameters:

• model_file (str, optional) – The filename that the model to be saved with or loaded from.

• hyperparameters (Dict[str, Any] | mlatom.models.hyperparameters, optional) – Updates the hyperparameters of the model with provided.

• verbose (int, optional) – 0 for silence, 1 for verbosity.

reset()

Resets model (deletes the ML model file from the hard disk).

train(molecular_database: molecular_database, property_to_learn: str = 'energy', xyz_derivative_property_to_learn: str = None, validation_molecular_database: molecular_database | str | None = 'sample_from_molecular_database', hyperparameters: Dict[str, Any] | hyperparameters = {}, spliting_ratio=0.8, save_model=True, log=True, check_point=False, use_last_model=False, summary_interval=1, validation_interval=1, save_interval=1, record_run_metadata=0) → None

Train the model with molecular database provided.

Parameters:

• molecular_database (mlatom.data.molecular_database) – The database of molecules for training.

• property_to_learn (str, optional) – The label of property to be learned in model training.

• xyz_derivative_property_to_learn (str, optional) – The label of XYZ derivative property to be learned.

predict(molecular_database: data.molecular_database = None, molecule: data.molecule = None, calculate_energy: bool = False, calculate_energy_gradients: bool = False, calculate_hessian: bool = False, property_to_predict: str | None = 'estimated_y', xyz_derivative_property_to_predict: str | None = None, hessian_to_predict: str | None = None) → None

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

• property_to_predict (str, optional) – The label name where the predicted properties to be saved.

• xyz_derivative_property_to_predict (str, optional) – The label name where the predicted XYZ derivatives to be saved.

• hessian_to_predict (str, optional) – The label name where the predicted Hessians to be saved.

MACE

!---------------------------------------------------------------------------!
! Interface_MACE: Interface between MACE and MLatom                         !
! Implementations by: Fuchun Ge, Yuxinxin Chen, Matheus O. Bispo            !
!---------------------------------------------------------------------------!

mlatom.interfaces.mace_interface.get_dataset_from_molDB(train_db: molecular_database, valid_db: molecular_database, valid_fraction: float = 0.1, config_type_weights: Dict = {'Default': 1.0}, energy_key: str = '', gradients_key: str = '') → Tuple[mace.tools.scripts_utils.SubsetCollection, Dict[int, float] | None]

Load training and test dataset from molecular database

class mlatom.interfaces.mace_interface.mace(model_file: str = None, device: str = None, hyperparameters: Dict[str, Any] | hyperparameters = {}, verbose=True)

Create an MACE model object.

Interfaces to MACE program.

Parameters:

• model_file (str, optional) – The filename that the model to be saved with or loaded from.

• device (str, optional) – Indicate which device the calculation will be run on. i.e. ‘cpu’ for CPU, ‘cuda’ for Nvidia GPUs. When not speficied, it will try to use CUDA if there exists valid CUDA_VISIBLE_DEVICES in the environ of system.

• hyperparameters (Dict[str, Any] | mlatom.models.hyperparameters, optional) – Updates the hyperparameters of the model with provided.

• verbose (int, optional) – 0 for silence, 1 for verbosity.

train(molecular_database: data.molecular_database, property_to_learn: str = 'energy', xyz_derivative_property_to_learn: str = None, validation_molecular_database: data.molecular_database | str | None = 'sample_from_molecular_database', spliting_ratio: float = 0.8, hyperparameters: Dict[str, Any] | models.hyperparameters = {}) → None

Train the model with molecular database provided.

Parameters:

• molecular_database (mlatom.data.molecular_database) – The database of molecules for training.

• property_to_learn (str, optional) – The label of property to be learned in model training.

• xyz_derivative_property_to_learn (str, optional) – The label of XYZ derivative property to be learned.

• validation_molecular_database (mlatom.data.molecular_database | str, optional) – Explicitly defines the database for validation, or use 'sample_from_molecular_database' to make it sampled from the training set.

• hyperparameters (Dict[str, Any] | mlatom.models.hyperparameters, optional) – Updates the hyperparameters of the model with provided.

predict(molecular_database: data.molecular_database = None, molecule: data.molecule = None, calculate_energy: bool = False, calculate_energy_gradients: bool = False, calculate_hessian: bool = False, property_to_predict: str | None = 'estimated_y', xyz_derivative_property_to_predict: str | None = None, hessian_to_predict: str | None = None, batch_size: int = 8) → None

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

• property_to_predict (str, optional) – The label name where the predicted properties to be saved.

• xyz_derivative_property_to_predict (str, optional) – The label name where the predicted XYZ derivatives to be saved.

• hessian_to_predict (str, optional) – The label name where the predicted Hessians to be saved.

• batch_size (int, optional) – The batch size for batch-predictions.

class mlatom.interfaces.mace_interface.CheckpointHandler_modified(*args: Any, **kwargs: Any)

sGDML

!---------------------------------------------------------------------------!
! Interface_sGDML: Interface between sGDML and MLatom                       !
! Implementations by: Fuchun Ge                                             !
!---------------------------------------------------------------------------!

class mlatom.interfaces.sgdml_interface.sgdml(model_file=None, hyperparameters={}, verbose=1, max_memory=None, max_processes=None, use_torch=False, lazy_training=False)

Create an sGDML model object.

Interfaces to sGDML.

Parameters:

• model_file (str, optional) – The filename that the model to be saved with or loaded from.

• hyperparameters (Dict[str, Any] | mlatom.models.hyperparameters, optional) – Updates the hyperparameters of the model with provided.

• verbose (int, optional) – 0 for silence, 1 for verbosity.

Hyperparameters:

Please refer to the sgdml manual

no_E: mlatom.models.hyperparameter(value=False) gdml: mlatom.models.hyperparameter(value=False) perms: mlatom.models.hyperparameter(value=None) sigma: mlatom.models.hyperparameter(value=None) E_cstr: mlatom.models.hyperparameter(value=False) cprsn: mlatom.models.hyperparameter(value=False)

reset()

Resets model (deletes the ML model file from the hard disk).

train(molecular_database: molecular_database, property_to_learn: str = 'energy', xyz_derivative_property_to_learn: str = None, validation_molecular_database: molecular_database | str | None = None, hyperparameters: Dict[str, Any] | hyperparameters = {}, spliting_ratio=0.8, save_model=True, task_dir=None, overwrite=False, max_memory=None, max_processes=None, use_torch=None, lazy_training=None)

Train the model with molecular database provided.

Parameters:

• molecular_database (mlatom.data.molecular_database) – The database of molecules for training.

• property_to_learn (str, optional) – The label of property to be learned in model training.

• xyz_derivative_property_to_learn (str, optional) – The label of XYZ derivative property to be learned.

predict(molecular_database: data.molecular_database = None, molecule: data.molecule = None, calculate_energy: bool = False, calculate_energy_gradients: bool = False, calculate_hessian: bool = False, property_to_predict: str | None = 'estimated_y', xyz_derivative_property_to_predict: str | None = None, hessian_to_predict: str | None = None, batch_size: int = 65536) → None

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

• property_to_predict (str, optional) – The label name where the predicted properties to be saved.

• xyz_derivative_property_to_predict (str, optional) – The label name where the predicted XYZ derivatives to be saved.

• hessian_to_predict (str, optional) – The label name where the predicted Hessians to be saved.

• batch_size (int, optional) – The batch size for batch-predictions.

Gaussian

!---------------------------------------------------------------------------!
! gaussian: interface to the Gaussian program                               !
! Implementations by: Pavlo O. Dral, Peikun Zheng, Yi-Fan Hou               !
!---------------------------------------------------------------------------!

class mlatom.interfaces.gaussian_interface.gaussian_methods(method='B3LYP/6-31G*', additional_input='', nthreads=None, save_files_in_current_directory=False, working_directory=None, **kwargs)

Gaussian interface

Parameters:

• method (str) – Method to use

• nthreads (int) – equivalent to %proc in Gaussian input file

• save_files_in_current_directory (bool) – whether to keep input and output files, default 'False'

• working_directory (str) – path to the directory where the program output files and other tempory files are saved, default 'None'

Note

The format of method should be the same as that in Gaussian, e.g., 'B3LYP/6-31G*'

predict(molecular_database=None, molecule=None, nstates=1, current_state=0, calculate_energy=True, calculate_energy_gradients=False, calculate_hessian=False, calculate_dipole_derivatives=False, gaussian_keywords=None)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

• nstates (int) – number of electronic structure states (default: 1, ground state)

• current_state (int) – default is the ground state (for nstates=1) or the first excited state (nstates > 1)

• gaussian_keywords (some type) – # needs to be documented.

mlatom.interfaces.gaussian_interface.parse_gaussian_output(filename=None, molecule=None)

Parsing Gaussian output file and return a molecule obj :param filename: Name of Gaussian output file (e.g. benzene.log or benzene.out) :return: molecule if molecule is not provided

Orca

!---------------------------------------------------------------------------!
! orca: interface to the ORCA program                                       !
! Implementations by: Yuxinxin Chen                                         !
!---------------------------------------------------------------------------!

class mlatom.interfaces.orca_interface.orca_methods(method='wb97x/6-31G*', **kwargs)

ORCA interface

Parameters:

• method (str) – method used the same as in ORCA, e.g. 'B3LYP/6-31G*' (case insensitive). This is the first line in orca input and you can also store the whole first line here.

• save_files_in_current_directory (bool) – whether to keep input and output files, default 'False'

• working_directory (str) – path to the directory where the program output files and other tempory files are saved, default 'None'

• nthreads (int) – equivalent to %pal nprocs in ORCA input file

• nthreads_list (list) – a list of number of nthreads used in CCSD(T)*/CBS method. The order should be [mp2_tz, mp2_qz, dlpno_normal_dz, dlpno_normal_tz, dlpno_tight_dz]

• additional_keywords (list) – list of keywords(str) to be added to orca input (first line)

• input_file (str) – name of your orca input file. Default will use “[molecule number]_”

• output_keywords (list) – list of keywords that you want to extract from output file. (Currently customized keywords are only supported in energy calculation and in property.txt file)

Note

When using CCSD(T)*/CBS for calculation, please make sure the 'nthreads' you used for each method will not cause memory exceeding. We suggest using 'nthreads_list' to properly set 'nthreads' for each component method in the order: [MP2/cc-pVTZ, MP2/cc-pVQZ, DLPNO-CCSD(T)-normalPNO/cc-pVDZ, DLPNO-CCSD(T)-normalPNO/cc-pVTZ, DLPNO-CCSD(T)-tightPNO/cc-pVTZ] If only 'nthreads' is set, all component methods would use the same number of threads.

predict(molecular_database=None, molecule=None, calculate_energy=True, calculate_energy_gradients=False, calculate_hessian=False, **kwargs)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

• **kwargs – # needs to be documented.

class mlatom.interfaces.orca_interface.ccsdtstarcbs(**kwargs)

predict(molecular_database, **kwargs)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

DFT-D4

!---------------------------------------------------------------------------!
! dftd4: interface to the dftd4 program                                     !
! Implementations by: Pavlo O. Dral & Peikun Zheng                          !
!---------------------------------------------------------------------------!

class mlatom.interfaces.dftd4_interface.dftd4_methods(functional=None, save_files_in_current_directory=True, working_directory=None, **kwargs)

DFT-D4 interface

Parameters:

• functional (str) – functional to use

• save_files_in_current_directory (bool) – whether to keep input and output files, default 'True'

• working_directory (str) – path to the directory where the program output files and other tempory files are saved, default 'None'

Note

The default DFT-D4 implementation provides a shared memory parallelisation for CPUs. They offer openMP parallelisation, which is not implemented here currently. For more discussion, please refer to https://github.com/dftd4/dftd4/issues/20.

predict(molecular_database=None, molecule=None, calculate_energy=True, calculate_energy_gradients=False, calculate_hessian=False, nstates=1, **kwargs)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

PySCF

!---------------------------------------------------------------------------!
! Interface_PySCF: interface to the PySCF program                           !
! Implementations by: Yuxinxin Chen                                         !
!---------------------------------------------------------------------------!

class mlatom.interfaces.pyscf_interface.OMP_pyscf

class mlatom.interfaces.pyscf_interface.pyscf_methods(method='B3LYP/6-31g', nthreads=None, **kwargs)

PySCF interface

Parameters:

• method (str) – Method to use

• nthreads (int) – Set the number of OMP threads

Note

Methods supported:

Energy: HF, MP2, DFT, CISD, FCI, CCSD/CCSD(T), TD-DFT/TD-HF

Gradients: HF, MP2, DFT, CISD, CCSD, RCCSD(T), TD-DFT/TD-HF

Hessian: HF, DFT

predict(molecule=None, molecular_database=None, calculate_energy=True, calculate_energy_gradients=False, calculate_hessian=False, nstates=1, current_state=1, **kwargs)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

• **kwargs – # needs to be documented.

Sparrow

!---------------------------------------------------------------------------!
! sparrow: interface to the Sparrow program                                 !
! Implementations by: Pavlo O. Dral & Peikun Zheng                          !
!---------------------------------------------------------------------------!

class mlatom.interfaces.sparrow_interface.sparrow_methods(method='ODM2*', read_keywords_from_file='', save_files_in_current_directory=False, working_directory=None, **kwargs)

Sparrow interface

Parameters:

• method (str) – method to use

• read_keywords_from_file (str) – keywords used in Sparrow

• save_files_in_current_directory (bool) – whether to keep input and output files, default 'False'

• working_directory (str) – path to the directory where the program output files and other tempory files are saved, default 'None'

Note

Methods supported:

Energy: DFTB0, DFTB2, DFTB3 MNDO, MNDO/d, AM1, PM3, PM6, RM1, OM2, OM3, ODM2*, ODM3* AIQM1

Gradients: DFTB0, DFTB2, DFTB3 MNDO, MNDO/d, AM1, PM3, PM6, RM1

predict(molecular_database=None, molecule=None, calculate_energy=True, calculate_energy_gradients=False, calculate_hessian=False, **kwargs)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

xTB

!---------------------------------------------------------------------------!
! xtb: interface to the xtb program                                         !
! Implementations by: Pavlo O. Dral                                         !
!---------------------------------------------------------------------------!

class mlatom.interfaces.xtb_interface.xtb_methods(method='GFN2-xTB', read_keywords_from_file='', nthreads=None, **kwargs)

xTB interface

Parameters:

• method (str) – xTB methods

• read_keywords_from_file (str) – keywords used in xTB

Note

GFN2-xTB and GFN2-xTB* (remove D4 from GFN2-xTB) are available.

Examples:

from ml.interfaces.xtb import xtb_methods()

# read molecule from xyz file
mol = ml.data.molecule()
mol.read_from_xyz_file('sp.xyz')

# initialize xtb methods
model = xtb_methods(method='GFN2-xTB)

# calculate energy, gradients and hessian
model.predict(molecule=mol,
            calculate_energy_gradients=True,
            calculate_hessian=True)
print(mol.energy)

predict(molecular_database=None, molecule=None, calculate_energy=True, calculate_energy_gradients=False, calculate_hessian=False, calculate_dipole_derivatives=False, calculate_polarizability_derivatives=False)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

MNDO

!---------------------------------------------------------------------------!
! mndo: interface to the MNDO program                                       !
! Implementations by: Pavlo O. Dral, Peikun Zheng, and Lina Zhang           !
!---------------------------------------------------------------------------!

class mlatom.interfaces.mndo_interface.mndo_methods(method='ODM2*', read_keywords_from_file='', save_files_in_current_directory=True, working_directory=None, **kwargs)

MNDO interface

Parameters:

• method (str) – method used in MNDO

• read_keywords_from_file (str) – keywords used in MNDO

• save_files_in_current_directory (bool) – whether to keep input and output files, default 'True'

• working_directory (str) – path to the directory where the program output files and other tempory files are saved, default 'None'

predict(molecular_database=None, molecule=None, nstates=1, current_state=0, calculate_energy=True, calculate_energy_gradients=False, calculate_hessian=False, calculate_dipole_derivatives=False, calculate_nacv=False, read_density_matrix=False)

Make predictions for molecular geometries with the model.

Parameters:

• molecular_database (mlatom.data.molecular_database, optional) – A database contains the molecules whose properties need to be predicted by the model.

• molecule (mlatom.models.molecule, optional) – A molecule object whose property needs to be predicted by the model.

• calculate_energy (bool, optional) – Use the model to calculate energy.

• calculate_energy_gradients (bool, optional) – Use the model to calculate energy gradients.

• calculate_hessian (bool, optional) – Use the model to calculate energy hessian.

Simulations

!---------------------------------------------------------------------------!
! simulations: Module for simulations                                       !
! Implementations by: Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Yuxinxin Chen   !
!---------------------------------------------------------------------------!

Geomopt, freq, DMC

class mlatom.simulations.optimize_geometry(model=None, model_predict_kwargs={}, initial_molecule=None, molecule=None, ts=False, program=None, optimization_algorithm=None, maximum_number_of_steps=None, convergence_criterion_for_forces=None, working_directory=None, print_properties=None, dump_trajectory_interval=None, filename=None, format='json', **kwargs)

Geometry optimization.

Parameters:

• model (mlatom.models.model or mlatom.models.methods) – any model or method which provides energies and forces.

• initial_molecule (mlatom.data.molecule) – the molecule object to optimize.

• ts (bool, optional) – whether to do transition state search. Currently only be done with program=Gaussian, ASE and geometric.

• program (str, optional) – the engine used in geometry optimization. Currently supports Gaussian, ASE, scipy and PySCF.

• optimization_algorithm (str, optional) – the optimization algorithm used in ASE. Default value: LBFGS (ts=False), dimer (ts=True).

• maximum_number_of_steps (int, optional) – the maximum number of steps for ASE, SciPy and geometric. Default value: 200.

• convergence_criterion_for_forces (float, optional) – forces convergence criterion in ASE. Default value: 0.02 eV/Angstroms.

• working_directory (str, optional) – working directory. Default value: ‘.’, i.e., current directory.

• constraints (dict, optional) – constraints for geometry optimization. Currently only available with program=ASE and program=geometric. For program=ASE, constraints follows the same conventions as in ASE: constraints={'bonds':[[target,[index0,index1]], ...],'angles':[[target,[index0,index1,index2]], ...],'dihedrals':[[target,[index0,index1,index2,index3]], ...]} (check FixInternals class in ASE for more information). For program=geometric, the name of constraint file should be provided and please refer to constrained optimization for the format of the constraint file.

• print_properties (None or str, optional) – properties to print. Default: None. Possible ‘all’.

• dump_trajectory_interval (int, optional) – dump trajectory at every time step (1). Set to None to disable dumping (default).

• filename (str, optional) – the file that saves the dumped trajectory.

• format (str, optional) – format in which the dumped trajectory is saved.

Examples:

# Initialize molecule
mol = ml.data.molecule()
mol.read_from_xyz_file(filename='ethanol.xyz')
# Initialize methods
aiqm1 = ml.models.methods(method='AIQM1', qm_program='MNDO')
# Run geometry optimization
geomopt = ml.simulations.optimize_geometry(model = aiqm1, initial_molecule=mol, program = 'ASE')
# Get the optimized geometry, energy, and gradient
optmol = geomopt.optimized_molecule
geo = optmol.get_xyz_coordinates()
energy = optmol.energy
gradient = optmol.get_energy_gradients()

class mlatom.simulations.freq(model=None, model_predict_kwargs={}, molecule=None, program=None, ir=False, raman=False, normal_mode_normalization='mass deweighted normalized', anharmonic=False, anharmonic_kwargs={}, working_directory=None)

Frequence analysis.

Parameters:

• model (mlatom.models.model or mlatom.models.methods) – any model or method which provides energies and forces and Hessian.

• molecule (mlatom.data.molecule) – the molecule object with necessary information.

• program (str, optional) – the engine used in frequence analysis through modified TorchANI (if Gaussian not found or any other string is given), pyscf or Gaussian interfaces.

• normal_mode_normalization (str, optional) – normal modes output scheme. It should be one of: mass weighted normalized, mass deweighted unnormalized, and mass deweighted normalized (default).

• anharmonic (bool) – whether to do anharmonic frequence calculation.

• working_directory (str, optional) – working directory. Default value: ‘.’, i.e., current directory.

Examples:

# Initialize molecule
mol = ml.data.molecule()
mol.read_from_xyz_file(filename='ethanol.xyz')
# Initialize methods
aiqm1 = ml.models.methods(method='AIQM1', qm_program='MNDO')
# Run frequence analysis
ml.simulations.freq(model=aiqm1, molecule=mol, program='ASE')
# Get frequencies
frequencies = mol.frequencies

class mlatom.simulations.thermochemistry(model=None, molecule=None, program=None, ir=False, raman=False, normal_mode_normalization='mass deweighted normalized')

Thermochemical properties calculation.

Parameters:

• model (mlatom.models.model or mlatom.models.methods) – any model or method which provides energies and forces and Hessian.

• molecule (mlatom.data.molecule) – the molecule object with necessary information.

• program (str) – the engine used in thermochemical properties calculation. Currently support Gaussian and ASE.

• normal_mode_normalization (str, optional) – normal modes output scheme. It should be one of: mass weighted normalized, mass deweighted unnormalized, and mass deweighted unnormalized (default).

# Initialize molecule
mol = ml.data.molecule()
mol.read_from_xyz_file(filename='ethanol.xyz')
# Initialize methods
aiqm1 = ml.models.methods(method='AIQM1', qm_program='MNDO')
# Run thermochemical properties calculation
ml.simulations.thermochemistry(model=aiqm1, molecule=mol, program='ASE')
# Get ZPE and heat of formation
ZPE = mol.ZPE
Hof = mol.DeltaHf298

The thermochemical properties available in molecule object after the calculation:

• ZPE: Zero-point energy

• DeltaE2U: Thermal correction to Energy (only available in Gaussian)

• DeltaE2H: Thermal correction to Enthalpy (only available in Gaussian)

• DeltaE2G: Thermal correction to Gibbs free energy (only available in Gaussian)

• U0: Internal energy at 0K

• H0: Enthalpy at 0K

• U: Internal energy (only available in Gaussian)

• H: Enthalpy

• G: Gibbs free energy

• S: Entropy (only available in Gaussian)

• atomization_energy_0K

• ZPE_exclusive_atomization_energy_0K

• DeltaHf298: Heat of formation at 298 K

class mlatom.simulations.dmc(model: model, initial_molecule: molecule = None, initial_molecular_database: molecular_database = None, energy_scaling_factor: float = 1.0)

Run diffusion Monte Carlo simulation for molecule(s) using PyVibDMC.

Parameters:

• model (mlatom.models.model) – The potential energy surfaces model. The unit should be Hartree, otherwise a correct energy_scaling_factor need to be set.

• initial_molecule (mlatom.data.molecule) – The initial geometry for the walkers. Usually a energy minimum geometry should be provided. By default every coordinate will be scaled by 1.01 to make it slightly distorted.

• energy_scaling_factor (float, optional) – A factor that will be multiplied to the model’s energy pridiction.

run(run_dir: str = 'DMC', weighting: str = 'discrete', number_of_walkers: int = 5000, number_of_timesteps: int = 10000, equilibration_steps: int = 500, dump_trajectory_interval: int = 500, dump_wavefunction_interval: int = 1000, descendant_weighting_steps: int = 300, time_step: float = 0.024188843265857, initialize: bool = False)

Run the DMC simulation.

Parameters:

• run_dir (str) – The folder for the output files.

• weighting (str) – 'discrete' or 'continuous'. 'continuous' keeps the ensemble size constant.

• number_of_walkers (int) – The number of geometries exploring the potential surface.

• number_of_timesteps (int) – The number of steps the simulation will go.

• equilibration_steps (int) – The number of steps for equilibration.

• dump_trajectory_interval (int) – The interval for dumping walkers’ trajectories.

• dump_wavefunction_interval (int) – The interval for collecting wave function.

• descendant_weighting_steps (int) – The number of time steps for descendant weighting per wave function.

• time_step (float) – The length of each time step in fs.

load(filename)

Load previous simulation results from a HDF5 file.

get_zpe(start_step=1000) → float

Return calculated zero-point energy in Hartree.

Parameters:

start_step (int) – The starting step for averaging the energies.

mlatom.simulations.numerical_gradients(molecule, model, displacement=1e-05, model_kwargs={}, return_molecular_database=False, nthreads=None)

Calculate numerical gradients. Two-point numerical differentiation is used and the required single-point calculations are run in parallel.

Parameters:

• molecule (mlatom.data.molecule) – the molecule object.

• model (mlatom.models.model or mlatom.models.methods) – any model or method which provides energies (takes molecule as an argument).

• displacement (float, optional) – displacement of nuclear coordinates in Angstrom (default: 1e-5).

• model_kwargs (dict, optional) – kwargs to be passed to model (except for molecule).

• return_molecular_database (bool, optional) – whether to return the mlatom.data.molecular_database with the displaced geometries and energies (default: False).

• nthreads (int, optional) – number of threads (default: None, using all threads it can find).

mlatom.simulations.numerical_hessian(molecule, model, displacement=0.000529167, displacement4grads=1e-05, model_kwargs={})

Calculate numerical Hessians. Two-point numerical differentiation is used and the required single-point calculations are run in parallel.

Parameters:

• molecule (mlatom.data.molecule) – the molecule object.

• model (mlatom.models.model or mlatom.models.methods) – any model or method which provides energies (takes molecule as an argument).

• displacement (float, optional) – displacement of nuclear coordinates in Angstrom (default: 5.29167e-4).

• displacement4grads (float, optional) – displacement of nuclear coordinates in Angstrom (default: 1e-5) when calculating gradients.

• model_kwargs (dict, optional) – kwargs to be passed to model (except for molecule).

Initial conditions

mlatom.initial_conditions.generate_initial_conditions(molecule=None, generation_method=None, number_of_initial_conditions=1, file_with_initial_xyz_coordinates=None, file_with_initial_xyz_velocities=None, eliminate_angular_momentum=True, degrees_of_freedom=None, initial_temperature=None, initial_kinetic_energy=None, use_hessian=False, reaction_coordinate_momentum=True, filter_by_energy_window=False, window_filter_kwargs={}, random_seed=None)

Generate initial conditions

Parameters:

• molecule (data.molecule) – molecule with necessary information

• generation_method (str) – initial condition generation method, see below the table

• number_of_initial_conditions (int) – number of initial conditions to generate, 1 by default

• file_with_initial_xyz_coordinates (str) – file with initial xyz coordinates, only valid for generation_method='user-defined'

• file_with_initial_xyz_velocities (str) – file with initial xyz velocities, only valid for generation_method='user-defined'

• eliminate_angular_momentum (bool) – remove angular momentum from velocities, valid for generation_method='random' and generation_method='wigner'

• degrees_of_freedom (int) – degrees of freedom of the molecule, by default remove translational and rotational degrees of freedom. It can be a negative value, which means that some value is subtracted from 3*Natoms

• initial_temperature (float) – initial temperature in Kelvin, control random initial velocities

• initial_kinetic_energy (float) – initial kinetic energy in Hartree, control random initial velocities

• random_seed (int) – random seed for numpy random number generator (do not use unless you want to obtain the same results every time)

• filter_by_energy_window (bool) – filter by excitation energy window

• window_filter_kwargs (dict) – keyword arguments for filtering the energy window, see below the table

generation_method	description
'user-defined' (default)	use user-defined initial conditions
'random'	generate random velocities
'maxwell-boltzmann'	randomly generate initial velocities from Maxwell-Boltzmann distribution
'wigner'	use Wigner sampling as implemented in Newton-X

window_filter_kwargs	description
model	model or method that can calculate excitation energies and oscillator strengths
model_predict_kwargs	keyword arguments for above model, typically nstates specifying how many states to calculate
target_excitation_energy (float)	in eV
window_half_width (float)	in eV
random_seed (int)	random seed for numpy random number generator (do not use unless you want to obtain the same results every time)

Returns:

A molecular database (ml.data.molecular_database) with number_of_initial_conditions initial conditions

Examples:

# Use user-defined initial conditions
init_cond_db = ml.generate_initial_conditions(molecule = mol,
                                              generation_method = 'user-defined',
                                              file_with_initial_xyz_coordinates = 'ethanol.xyz',
                                              file_with_initial_xyz_velocities  = 'ethanol.vxyz',
                                              number_of_initial_conditions = 1)
# Generate random velocities
init_cond_db = ml.generate_initial_conditions(molecule = mol,
                                              generation_method = 'random',
                                              initial_temperature = 300,
                                              number_of_initial_conditions = 1)
# Use Wigner sampling
init_cond_db = ml.generate_initial_conditions(molecule = mol,
                                              generation_method = 'wigner',
                                              number_of_initial_conditions = 1)

# Sample with filtering by excitation energy window. Requires the model for calculating excitation energies and oscillator strengths.
model = ml.models.methods(method='AIQM1')
model_predict_kwargs={'nstates':9} # requests calculation of 9 electronic states
window_filter_kwargs={'model':model,
                      'model_predict_kwargs':model_predict_kwargs,
                      'target_excitation_energy':5.7, # eV
                      'window_half_width':0.1, # eV}
init_cond_db = ml.generate_initial_conditions(molecule=mol,
                                            generation_method='wigner',
                                            number_of_initial_conditions=5,
                                            initial_temperature=0,
                                            random_seed=0,
                                            use_hessian=False,
                                            filter_by_energy_window=True,
                                            window_filter_kwargs=window_filter_kwargs)

Note

Wigner sampling needs Hessian matrix. You can use ml.models.methods.predict(molecule=mol,calculate_hessian=True) to get Hessian matrix.

Molecular dynamics

!---------------------------------------------------------------------------!
! md: Module for molecular dynamics                                         !
! Implementations by: Yi-Fan Hou & Pavlo O. Dral                            !
!---------------------------------------------------------------------------!

class mlatom.md.md(model=None, model_predict_kwargs={}, molecule_with_initial_conditions=None, molecule=None, ensemble='NVE', thermostat=None, time_step=0.1, maximum_propagation_time=1000, dump_trajectory_interval=None, filename=None, format='h5md', stop_function=None, stop_function_kwargs=None)

Molecular dynamics

Parameters:

• model (mlatom.models.model or mlatom.models.methods) – Any model or method which provides energies and forces.

• molecule_with_initial_conditions (data.molecule) – The molecule with initial conditions.

• ensemble (str, optional) – Which kind of ensemble to use.

• thermostat (thermostat.Thermostat) – The thermostat applied to the system.

• time_step (float) – Time step in femtoseconds.

• maximum_propagation_time (float) – Maximum propagation time in femtoseconds.

• dump_trajectory_interval (int, optional) – Dump trajectory at which interval. Set to None to disable dumping.

• filename (str, optional) – The file that saves the dumped trajectory

• format (str, optional) – Format in which the dumped trajectory is saved

• stop_function (any, optional) – User-defined function that stops MD before maximum_propagation_time

• stop_function_kwargs (Dict, optional) – Kwargs of stop_function

ensemble	description
'NVE' (default)	Microcanonical (NVE) ensemble
'NVT'	Canonical (NVT) ensemble

thermostat	description
ml.md.Andersen_thermostat	Andersen thermostat
ml.md.Nose_Hoover_thermostat	Hose-Hoover thermostat
None (default)	No thermostat is applied

For theoretical details, see and cite original paper.

Examples:

# Initialize molecule
mol = ml.data.molecule()
mol.read_from_xyz_file(filename='ethanol.xyz')
# Initialize methods
aiqm1 = ml.models.methods(method='AIQM1')
# User-defined initial condition
init_cond_db = ml.generate_initial_conditions(molecule = mol,
                                              generation_method = 'user-defined',
                                              file_with_initial_xyz_coordinates = 'ethanol.xyz',
                                              file_with_initial_xyz_velocities  = 'ethanol.vxyz')
init_mol = init_cond_db.molecules[0]
# Initialize thermostat
nose_hoover = ml.md.Nose_Hoover_thermostat(temperature=300,molecule=init_mol,degrees_of_freedom=-6)
# Run dynamics
dyn = ml.md(model=aiqm1,
            molecule_with_initial_conditions = init_mol,
            ensemble='NVT',
            thermostat=nose_hoover,
            time_step=0.5,
            maximum_propagation_time = 10.0)
# Dump trajectory
traj = dyn.molecular_trajectory
traj.dump(filename='traj', format='plain_text')
traj.dump(filename='traj.h5', format='h5md')

Note

Trajectory is saved in ml.md.molecular_trajectory, which is a ml.data.molecular_trajectory class

Warning

In MLatom, energy unit is Hartree and distance unit is Angstrom. Make sure that the units in your model are consistent.

class Andersen_thermostat(**kwargs)

Andersen thermostat object

Parameters:

• gamma (float) – collision rate in fs^{-1}, 0.2 by default

• temperature (float) – System temperature to equilibrate in Kelvin, 300 by default

class Nose_Hoover_thermostat(**kwargs)

Nose-Hoover thermostat object

Parameters:

• nose_hoover_chain_length (int) – Nose Hoover chain length, should be a positive number, 3 by default

• multiple_time_step (int) – Multiple time step, should be a positive number, 3 by default

• number_of_yoshida_suzuki_steps (int) – Number of Yoshida Suzuki steps, can be any in (1,3,5,7), 7 by default

• nose_hoover_chain_frequency (float) – Nose-Hoover chain frequency in $fs^{-1}$, 0.0625 by default, should be comparable to the frequency you want to equilibrate

• temperature (float) – System temperature to equilibrate in Kelvin, 300 by default

• molecule (data.molecule) – The molecule to be equilibrated

• degrees_of_freedom – Degrees of freedom of the system

Surface-hopping dynamics

!---------------------------------------------------------------------------!
! namd: Module for nonadiabatic molecular dynamics                          !
! Implementations by: Lina Zhang & Pavlo O. Dral                            !
!---------------------------------------------------------------------------!

class mlatom.namd.surface_hopping_md(model=None, model_predict_kwargs={}, molecule_with_initial_conditions=None, molecule=None, ensemble='NVE', thermostat=None, time_step=0.1, maximum_propagation_time=100, dump_trajectory_interval=None, filename=None, format='h5md', stop_function=None, stop_function_kwargs=None, hopping_algorithm='LZBL', nstates=None, initial_state=None, random_seed=<function generate_random_seed>, prevent_back_hop=False, reduce_memory_usage=False, rescale_velocity_direction='along velocities', reduce_kinetic_energy=False)

Surface-hopping molecular dynamics

Parameters:

• model (mlatom.models.model or mlatom.models.methods) – Any model or method which provides energies and forces.

• model_predict_kwargs (Dict, optional) – Kwargs of model prediction

• molecule_with_initial_conditions (data.molecule) – The molecule with initial conditions.

• molecule (data.molecule) – Work the same as molecule_with_initial_conditions

• ensemble (str, optional) – Which kind of ensemble to use.

• thermostat (thermostat.Thermostat) – The thermostat applied to the system.

• time_step (float) – Time step in femtoseconds.

• maximum_propagation_time (float) – Maximum propagation time in femtoseconds.

• dump_trajectory_interval (int, optional) – Dump trajectory at which interval. Set to None to disable dumping.

• filename (str, optional) – The file that saves the dumped trajectory

• format (str, optional) – Format in which the dumped trajectory is saved

• stop_function (any, optional) – User-defined function that stops MD before maximum_propagation_time

• stop_function_kwargs (Dict, optional) – Kwargs of stop_function

• hopping_algorithm (str, optional) – Surface hopping algorithm

• nstates (int) – Number of states

• initial_state (int) – Initial state

• random_seed (int) – Random seed

• prevent_back_hop (bool, optional) – Whether to prevent back hopping

• rescale_velocity_direction (string, optional) – Rescale velocity direction

• reduce_kinetic_energy (bool, optional) – Whether to reduce kinetic energy

ensemble	description
'NVE' (default)	Microcanonical (NVE) ensemble
'NVT'	Canonical (NVT) ensemble

thermostat	description
ml.md.Andersen_thermostat	Andersen thermostat
ml.md.Nose_Hoover_thermostat	Hose-Hoover thermostat
None (default)	No thermostat is applied

For theoretical details, see and cite original paper (to be submitted).

• Lina Zhang, Sebastian Pios, Mikołaj Martyka, Fuchun Ge, Yi-Fan Hou, Yuxinxin Chen, Joanna Jankowska, Lipeng Chen, Mario Barbatti, Pavlo O. Dral. MLatom software ecosystem for surface hopping dynamics in Python with quantum mechanical and machine learning methods. 2024, to be submitted. Preprint on arXiv: https://arxiv.org/abs/2404.06189.

Examples:

# Propagate multiple LZBL surface-hopping trajectories in parallel
# .. setup dynamics calculations
namd_kwargs = {
            'model': aiqm1,
            'time_step': 0.25,
            'maximum_propagation_time': 5,
            'hopping_algorithm': 'LZBL',
            'nstates': 3,
            'initial_state': 2,
            }

# .. run trajectories in parallel
dyns = ml.simulations.run_in_parallel(molecular_database=init_cond_db,
                                    task=ml.namd.surface_hopping_md,
                                    task_kwargs=namd_kwargs,
                                    create_and_keep_temp_directories=True)
trajs = [d.molecular_trajectory for d in dyns]

# Dump the trajectories
itraj=0
for traj in trajs:
    itraj+=1
    traj.dump(filename=f"traj{itraj}.h5",format='h5md')

# Analyze the result of trajectories and make the population plot
ml.namd.analyze_trajs(trajectories=trajs, maximum_propagation_time=5)
ml.namd.plot_population(trajectories=trajs, time_step=0.25,
                    max_propagation_time=5, nstates=3, filename=f'pop.png',
                    pop_filename='pop.txt')

Note

Trajectory is saved in ml.md.molecular_trajectory, which is a ml.data.molecular_trajectory class

Warning

In MLatom, energy unit is Hartree and distance unit is Angstrom. Make sure that the units in your model are consistent.

Spectra

!---------------------------------------------------------------------------!
! spectra: Module for working with spectra                                  !
! Implementations by: Yi-Fan Hou, Fuchun Ge, Bao-Xin Xue, Pavlo O. Dral     !
!---------------------------------------------------------------------------!

class mlatom.spectra.uvvis(x=None, y=None, wavelengths_nm=None, energies_eV=None, molar_absorbance=None, cross_section=None, meta_data=None)

UV/Vis absorption spectrum class

Parameters:

• x (float, np.ndarray) – range of spectra (e.g., wavelength in nm, recommended, or energies in eV)

• y (float, np.ndarray) – user-provided intensities (e.g., molar absorpbance, recommended, or cross section)

• done (It is better to provide spectrum information explicitly so that the correct conversions to different units are)

• wavelengths_nm (float, np.ndarray) – range of wavelengths in nm

• energies_eV (float, np.ndarray) – range of energies in eV

• molar_absorbance (float, np.ndarray) – molar absorbance (extinction coefficients) in M^-1 cm^-1

• cross_section (float, np.ndarray) – cross section in A^2/molecule

• Also

• meta-data (the user is encouraged to provide the)

• meta_data (str) – meta data such as solvent, references, etc.

Example

uvvis = mlatom.spectra.uvvis(

wavelengths_nm = np.array(…), molar_absorbance = np.array(…), meta_data = ‘solvent: benzene, reference: DOI…’ )

# spectral properties can be accessed as: # uvvis.x is equivalent to what is provided by the user, e.g., wavelengths_nm or energies_eV # uvvis.y is equivalent to what is provided by the user, e.g., molar_absorbance or cross_section # wavelength range (float, np.ndarray) in nm uvvis.wavelengths_nm # molar absorbance (extinction coefficients) (float, np.ndarray) in M^-1 cm^-1 uvvis.molar_absorbance # energies corresponding to the wavelength range (float, np.ndarray), in eV uvvis.energies_eV # absorption cross-section (float, np.ndarray) in A^2/molecule uvvis.cross_section

classmethod spc(molecule=None, band_width=0.3, shift=0.0, refractive_index=1.0)

Single-point convolution (SPC) approach for obtaining UV/vis spectrum via calculating the extinction coefficient (and absorption cross section) from the single-point excited-state simulations for a single geometry Implementation follows http://doi.org/10.1007/s00894-020-04355-y

Parameters:

• molecule (mlatom.data.molecule) – molecule object with excitation_energies (in Hartree, not eV!) and oscillator_strengths

• wavelengths_nm (float, np.ndarray) – range of wavelengths in nm (default: np.arange(400, 800))

• band_width (float) – band width in eV (default: 0.3 eV)

• shift (float) – shift of excitation energies, eV (default: 0 eV)

• refractive_index (float) – refractive index (default: 1)

Example

uvvis = mlatom.spectra.uvvis.spc(

molecule=mol, wavelengths_nm=np.arange(100, 200), band_width=0.3)

# spectral properties can be accessed as: # uvvis.x is equivalent to uvvis.wavelengths_nm # uvvis.y is equivalent to uvvis.molar_absorbance # wavelength range (float, np.ndarray) in nm uvvis.wavelengths_nm # molar absorbance (extinction coefficients) (float, np.ndarray) in M^-1 cm^-1 uvvis.molar_absorbance # energies corresponding to the wavelength range (float, np.ndarray), in eV uvvis.energies_eV # absorption cross-section (float, np.ndarray) in A^2/molecule uvvis.cross_section # quick plot uvvis.plot(filename=’uvvis.png’)

classmethod spc_broadening_func(DeltaE, ff, wavelength_range, band_width, refractive_index=1, shift=0.0)

Spectrum convolution function

Parameters:

• band_width (float) – width of band

• DeltaE (float) – vertical excitation energy, eV

• ff (float) – oscillator strength

• wavelength_range (float, np.ndarray) – range of wavelengths

• refractive_index (float) – refractive index

• shift (float) – peak shift

Returns:

extinction coefficients in M^-1 cm^-1

Return type:

(float, np.ndarray)

classmethod nea(molecular_database=None, wavelengths_nm=None, broadening_width=0.05)

Nuclear ensemble approach (NEA) for obtaining UV/vis spectrum. Implementation follows Theor. Chem. Acc. 2012, 131, 1237.

Parameters:

• molecular_database (mlatom.data.molecular_database) – molecular_database object with molecules containing excitation_energies (in Hartree, not eV!) and oscillator_strengths

• wavelengths_nm (float, np.ndarray) – range of wavelengths in nm (default: determined automatically)

• broadening_width (float) – broadening factor in eV (default: 0.05 eV)

Example

uvvis = mlatom.spectra.uvvis.nea(molecular_database=db,

wavelengths_nm=wavelengths_nm, broadening_width=0.02)

# spectral properties can be accessed as: # uvvis.x is equivalent to uvvis.wavelengths_nm # uvvis.y is equivalent to uvvis.molar_absorbance # wavelength range (float, np.ndarray) in nm uvvis.wavelengths_nm # molar absorbance (extinction coefficients) (float, np.ndarray) in M^-1 cm^-1 uvvis.molar_absorbance # energies corresponding to the wavelength range (float, np.ndarray), in eV uvvis.energies_eV # absorption cross-section (float, np.ndarray) in A^2/molecule uvvis.cross_section # quick plot uvvis.plot(filename=’uvvis.png’)

Active learning

Initial data sampling

initdata_sampler can be:

• 'wigner'

• 'harmonic-quantum-boltzmann'

User-defined ML models

The user has the flexibility to create their own ML model class for AL. Minimum requirements to such a class:

• it must have the usual train and predict functions.

• the train function must accept molecular_database parameter.

• the predict function must accept molecule and/or molecular_database parameters.

The realistic, fully fledged example of how to create a usable ML model class is below (it is what we use in al routine!):

class my_model():
    def __init__(self, al_info = {}, model_file=None, device=None, verbose=False):
        import torch
        if device is None:
            device = 'cuda' if torch.cuda.is_available() else 'cpu'

        if model_file is None:
            if 'mlmodel_file' in al_info.keys():
                self.model_file = al_info['mlmodel_file']
            else:
                self.model_file = 'mlmodel'
                al_info['mlmodel_file'] = self.model_file
        else:
            self.model_file = model_file
            al_info['mlmodel_file'] = self.model_file
        if 'main_mlmodel_file' in al_info.keys():
            main_mlmodel_file = al_info['main_mlmodel_file']
        else:
            main_mlmodel_file = f'{self.model_file}.pt'
            al_info['main_mlmodel_file'] = main_mlmodel_file
        if 'aux_mlmodel_file' in al_info.keys():
            aux_mlmodel_file = al_info['aux_mlmodel_file']
        else:
            aux_mlmodel_file = f'aux_{self.model_file}.pt'
            al_info['aux_mlmodel_file'] = aux_mlmodel_file
        self.device = device
        self.verbose = verbose
        self.main_model = ml.models.ani(model_file=main_mlmodel_file,device=device,verbose=verbose)
        self.aux_model = ml.models.ani(model_file=aux_mlmodel_file,device=device,verbose=verbose)

    def train(self, molecular_database=None, al_info={}):
        if 'working_directory' in al_info.keys():
            workdir = al_info['working_directory']
            self.main_model.model_file = f'{workdir}/{self.model_file}.pt'
            self.aux_model.model_file = f'{workdir}/aux_{self.model_file}.pt'

        validation_set_fraction = 0.1
        [subtraindb, valdb] = molecular_database.split(number_of_splits=2, fraction_of_points_in_splits=[1-validation_set_fraction, validation_set_fraction], sampling='random')

        # train the model on energies and gradients
        self.main_model = ml.models.ani(model_file=self.main_model.model_file,device=self.device,verbose=self.verbose)
        self.main_model.train(molecular_database=subtraindb,validation_molecular_database=valdb,property_to_learn='energy',xyz_derivative_property_to_learn='energy_gradients')

        # train the auxiliary model only on energies
        self.aux_model = ml.models.ani(model_file=self.aux_main_model.model_file,device=self.device,verbose=self.verbose)
        self.aux_model.train(molecular_database=subtraindb,validation_molecular_database=valdb,property_to_learn='energy')

        if not 'uq_threshold' in al_info.keys():
            self.predict(molecular_database=valdb)
            uqs = valdb.get_property('uq')
            al_info['uq_threshold'] = np.median(uqs) + 3*stats.calc_median_absolute_deviation(uqs)
        self.uq_threshold = al_info['uq_threshold']

        # if the models were trained successfully, let's update al info where we can find them
        al_info['main_mlmodel_file'] = self.main_model.model_file
        al_info['aux_mlmodel_file'] = self.aux_model.model_file

    def predict(self, molecule=None, molecular_database=None):

        # predict energies and gradients with the main model
        self.main_model.predict(molecule=molecule, molecular_database=molecular_database,property_to_predict='energy',xyz_derivative_property_to_predict='energy_gradients')

        # predict energies with the auxiliary model
        self.aux_model.predict(molecule=molecule, molecular_database=molecular_database,property_to_predict='aux_energy')

        # calculate uncertainties
        moldb = molecular_database
        if moldb is None:
            moldb = ml.molecular_database()

        for mol in moldb:
            mol.uq = abs(mol.energy - mol.aux_energy)
            if mol.uq > self.uq_threshold:
                mol.uncertain = True
            else:
                mol.uncertain = False

    # This are useful in some internal al routines, e.g., when we want to make predictions in parallel (if nthreads is not set properly, it may slow down al significantly!)
    @property
    def nthreads(self):
        return self.main_model.nthreads

    @nthreads.setter
    def nthreads(self, value):
        self.main_model.nthreads = value
        self.aux_model.nthreads  = value

ml.al(
    ...
    ml_model = my_model,
    # do not use my_model(...), if you want to pass any arguments, use ml_model_kwargs:
    ml_model_kwargs = {...}, # 'al_info' is unnecessary to include, it will be added automatically. If you supply 'al_info' key, it will overwrite the default one so use if you know what you are doing.
    ...
)

As you can see, it is helpful (but not required) if the __init__ and train functions of the ML model class also accept the al_info parameter which can be used to pass information during active learning from one routine to another.

Sampler

Here is a realistic example of the sampler function used in the physics-informed active learning:

def my_sampler(al_info={}, ml_model=None, initcond_sampler=None, initcond_sampler_kwargs={}, maximum_propagation_time=1000, time_step=0.1, ensemble='NVE', thermostat=None, dump_trajs=False, dump_trajectory_interval=None, stop_function=None, batch_parallelization=True):

    moldb2label = ml.data.molecular_database()

    # generate initial conditions
    if type(initcond_sampler) == str:
        if initcond_sampler.casefold() in ['wigner', 'harmonic-quantum-boltzmann']:
            initcond_sampler = ml.generate_initial_conditions
            initcond_sampler_kwargs['generation_method'] = initcond_sampler
    import inspect
    args, varargs, varkw, defaults = inspect.getargspec(initcond_sampler)
    # Do we need al_info below?
    if 'al_info' in args:
        initial_molecular_database = initcond_sampler(al_info=al_info, **initcond_sampler_kwargs)
    else:
        initial_molecular_database = initcond_sampler(**initcond_sampler_kwargs)

    # run MD in parallel to collect uncertain points
    if batch_parallelization: # Faster way to propagate many trajs with ML
        dyn = ml.md_parallel(model=ml_model,
                             molecular_database=initial_molecular_database,
                             ensemble=ensemble,
                             thermostat=thermostat,
                             time_step=time_step,
                             maximum_propagation_time=maximum_propagation_time,
                             dump_trajectory_interval=dump_trajectory_interval,
                             stop_function=stop_function)
        trajs = dyn.molecular_trajectory
        for itraj in range(len(trajs.steps[0])):
            print(f"Trajectory {itraj} number of steps: {trajs.traj_len[itraj]}")
            if trajs.steps[trajs.traj_len[itraj]][itraj].uncertain:
                print(f'Adding molecule from trajectory {itraj} at time {trajs.traj_len[itraj]*time_step} fs')
                moldb2label.molecules.append(trajs.steps[trajs.traj_len[itraj]][itraj])

            # Dump traj
            if dump_trajs:
                import os
                traj = ml.data.molecular_trajectory()
                for istep in range(trajs.traj_len[itraj]+1):

                    step = ml.data.molecular_trajectory_step()
                    step.step = istep
                    step.time = istep * time_step
                    step.molecule = trajs.steps[istep][itraj]
                    traj.steps.append(step)
                if 'working_directory' in al_info.keys():
                    dirname = f'{al_info['working_directory']}/trajs'
                else:
                    dirname = 'trajs'
                if not os.path.exists(dirname):
                    os.makedirs(dirname)
                traj.dump(f"{dirname}/traj{itraj}.h5",format='h5md')
    else:
        md_kwargs = {
                    'molecular_database': initial_molecular_database,
                    'model': ml_model,
                    'time_step': time_step,
                    'maximum_propagation_time': maximum_propagation_time,
                    'ensemble': ensemble,
                    'thermostat': thermostat,
                    'dump_trajectory_interval': dump_trajectory_interval,
                    'stop_function': stop_function
                    }
        dyns = ml.simulations.run_in_parallel(molecular_database=initial_molecular_database,
                                            task=ml.md,
                                            task_kwargs=md_kwargs,
                                            create_and_keep_temp_directories=False)
        trajs = [d.molecular_trajectory for d in dyns]
        itraj=0
        for traj in trajs:
            itraj+=1
            print(f"Trajectory {itraj} number of steps: {len(traj.steps)}")
            if traj.steps[-1].molecule.uncertain:
                print('Adding molecule from trajectory %d at time %.2f fs' % (itraj, traj.steps[-1].time))
                moldb2label.molecules.append(traj.steps[-1].molecule)

            # Dump traj
            if dump_trajs:
                import os
                if 'working_directory' in al_info.keys():
                    dirname = f'{al_info['working_directory']}/trajs'
                else:
                    dirname = 'trajs'
                if not os.path.exists(dirname):
                    os.makedirs(dirname)
                traj.dump(f"{dirname}/traj{itraj}.h5",format='h5md')
    # add the source of molecule
    for mol in moldb2label:
        mol.sampling = 'md'
    return moldb2label

ml.al(
    ...
    sampler=my_sampler,
    sampler_kwargs={'time_step': 0.5},
    ...
)

Analysis

!—————————————————————————! ! xyz: different operations on XYZ coordinates ! ! Implementations by: Fuchun Ge, Yi-Fan Hou, Pavlo O. Dral ! !—————————————————————————!

mlatom.xyz.rmsd(molecule1, molecule2, reorder=False, check_reflection=False, keep_stereo=False)

Calculate RMSD (root-mean-squared deviation) between two structures.

Parameters:

• molecule1 (required) – either molecule class instance or numpy array with xyz coordinates

• molecule2 (required) – either molecule class instance or numpy array with xyz coordinates

• reorder (bool or str, optional) – whether to try to reorder atoms to get smaller RMSD (default: False; other supported options are True which is equivalent to Hungarian)

• check_reflection (bool, optional) – check reflections (default: False).

• keep_stereo (bool, optional) – (default: False).

Example of the simple use:

rmsd = mlatom.xyz.rmsd(molecule1.xyz_coordinates, molecule2.xyz_coordinates)

Example of using Hungarian algorithm to check for homonuclear atom permutation and reflections:

rmsd = mlatom.xyz.rmsd(molecule1, molecule2, reorder=’Hungarian’, check_reflection=True)