#!/usr/bin/env python3
'''
.. code-block::
!---------------------------------------------------------------------------!
! sparrow: interface to the Sparrow program !
! Implementations by: Pavlo O. Dral & Peikun Zheng !
!---------------------------------------------------------------------------!
'''
import os
import numpy as np
from .. import constants, simulations, models
from ..decorators import doc_inherit
[文档]
class sparrow_methods(models.OMP_model, models.method_model):
'''
Sparrow interface
Arguments:
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
'''
bin_env_name = 'sparrowbin'
supported_methods = ['DFTB0', 'DFTB2', 'DFTB3', 'MNDO', 'MNDO/d', 'AM1', 'RM1', 'PM3', 'PM6', 'OM2', 'OM3', 'ODM2*', 'ODM3*', 'AIQM1']
availability_of_gradients_for_methods = {
'DFTB0': True, 'DFTB2': True, 'DFTB3': True,
'MNDO': True, 'MNDO/d': True, 'AM1': True, 'RM1': True, 'PM3': True, 'PM6': True,
'OM2': False, 'OM3': False, 'ODM2*': False, 'ODM3*': False,
'AIQM1': False}
def __init__(self, method='ODM2*', read_keywords_from_file='', save_files_in_current_directory=False, working_directory=None):
self.method = method
self.read_keywords_from_file = read_keywords_from_file
self.save_files_in_current_directory = save_files_in_current_directory
self.working_directory = working_directory
self.sparrowbin = self.get_bin_env_var()
if self.sparrowbin is None:
raise ValueError('Cannot find the Sparrow program, please set the environment variable: export sparrowbin=...')
if method.casefold() == 'odm2*': print(' !WARNING! ODM2* calculations will be performed with Sparrow which has no implementation of analytical gradients and excited-state property calculations with this Hamiltonian. If you have the MNDO program you might want to use it for such calculations. Alternatively, choose a newer AIQM-series methods such as AIQM2 that is not based on ODM2* but on GFN2-xTB. MNDO is not available on the XACS cloud.')
[文档]
@doc_inherit
def predict(self, molecular_database=None, molecule=None,
calculate_energy=True, calculate_energy_gradients=False, calculate_hessian=False,
**kwargs):
allowed_kwargs = {'nstates': 1, 'current_state': 0,
'calculate_dipole_derivatives': False,
'calculate_nacv': False, 'read_density_matrix': False}
for kwarg in kwargs:
if kwarg not in allowed_kwargs.keys():
raise ValueError(f"keyworded argument '{kwarg}={kwargs[kwarg]}' is not allowed in Sparrow interface")
elif kwargs[kwarg] != allowed_kwargs[kwarg]:
raise ValueError(f"keyworded argument '{kwarg}={kwargs[kwarg]}' is not allowed in Sparrow interface, only '{kwarg}={allowed_kwargs[kwarg]}' is allowed. You might want to use the MNDO interface.")
molDB = super().predict(molecular_database=molecular_database, molecule=molecule)
# Not very good method naming in Sparrow...
# ODM2 is not implemented, ODM2 is just ODM2*, same for ODM3/ODM3*
if self.method == 'ODM2*': method_to_pass = 'ODM2'
elif self.method == 'ODM3*': method_to_pass = 'ODM3'
else: method_to_pass = self.method
additional_sparrow_keywords = []
if self.read_keywords_from_file != '':
kw_file = self.read_keywords_from_file
with open(kw_file, 'r') as fkw:
for line in fkw:
additional_sparrow_keywords = line.split()
joined_args = ''.join(additional_sparrow_keywords)
if 'iop' in joined_args or 'job' in joined_args:
raise ValueError('Sparrow does not support mndo keywords. If you have the MNDO program you might want to use it for such calculations. Alternatively, choose a newer AIQM-series methods that are not based on ODM2* but on GFN2-xTB. MNDO is not available on the XACS cloud.')
imol = -1
for mol in molDB.molecules:
imol += 1
jmol = imol
if len(additional_sparrow_keywords) < imol+1: jmol = -1
mol.additional_sparrow_keywords = additional_sparrow_keywords[jmol]
import tempfile, subprocess
ii = 0
for mol in molDB.molecules:
with tempfile.TemporaryDirectory() as tmpdirname:
if self.save_files_in_current_directory: tmpdirname = '.'
if self.working_directory is not None:
tmpdirname = self.working_directory
if not os.path.exists(tmpdirname):
os.makedirs(tmpdirname)
tmpdirname = os.path.abspath(tmpdirname)
ii += 1
xyzfilename = f'{tmpdirname}/predict{ii}.xyz'
mol.write_file_with_xyz_coordinates(filename = xyzfilename)
sparrowargs = [self.sparrowbin,
'-x', xyzfilename,
'-c', '%d' % mol.charge,
'-s','%d' % mol.multiplicity,
'-M', method_to_pass,
'-o',]
if mol.multiplicity != 1:
sparrowargs.append('-u')
sparrowargs += additional_sparrow_keywords
if calculate_energy_gradients and self.availability_of_gradients_for_methods[self.method]:
sparrowargs += ['-G']
if calculate_hessian and self.availability_of_gradients_for_methods[self.method]:
sparrowargs += ['-H']
#cmd = ' '.join(sparrowargs)
proc = subprocess.Popen(sparrowargs, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=tmpdirname, universal_newlines=True)
#proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=tmpdirname, universal_newlines=True, shell=True)
#proc.wait()
outs,errs = proc.communicate()
#os.system(cmd + " &> sparrow.out")
mol.sparrow_scf_successful = False
# for readable in proc.stdout:
# if 'SCF converged!' in readable:
# mol.sparrow_scf_successful = True
if 'SCF converged!' in outs+errs:
mol.sparrow_scf_successful = True
if not mol.sparrow_scf_successful:
return
if calculate_energy:
mol.energy = np.loadtxt(f'{tmpdirname}/energy.dat', comments='#').tolist()
if calculate_energy_gradients:
if self.availability_of_gradients_for_methods[self.method]:
mol.energy_gradients = np.loadtxt(f'{tmpdirname}/gradients.dat', comments='#') / constants.Bohr2Angstrom
else:
save_files_in_current_directory = self.save_files_in_current_directory
self.save_files_in_current_directory = False
working_directory = self.working_directory
self.working_directory = None
_ = simulations.numerical_gradients(mol, self, 1e-5, model_kwargs = {'calculate_energy_gradients': False, 'calculate_hessian': False})
self.save_files_in_current_directory = save_files_in_current_directory
self.working_directory = working_directory
if calculate_hessian:
if self.availability_of_gradients_for_methods[self.method]:
mol.hessian = np.loadtxt(f'{tmpdirname}/hessian.dat', comments='#') / (constants.Bohr2Angstrom**2)
else:
save_files_in_current_directory = self.save_files_in_current_directory
self.save_files_in_current_directory = False
working_directory = self.working_directory
self.working_directory = None
_ = simulations.numerical_hessian(mol, self, 5.29167e-4, 1e-5, model_kwargs = {'calculate_energy_gradients': False, 'calculate_hessian': False})
self.save_files_in_current_directory = save_files_in_current_directory
self.working_directory = working_directory
if __name__ == '__main__':
pass