面跳跃动力学

In this tutorial we show how to perform nonadiabatic molecular dynamics (NAMD) and analysis of NAMD trajectories with MLatom. The simulations are only possible through the Python API. MLatom currently only supports NAC-free Landau–Zener–Belyev–Lebedev (LZBL) surface hopping.

You can run the TSH with MLatom for the following models:

  • CASSCF through the interface to COLUMBUS

  • ADC(2) through the interface to Turbomole

  • AIQM1/CI

  • MS-ANI

  • Any ML models that provides energies and forces for electronic states of interset.

Running NAMD dynamics

See our paper for more details (please also cite it if you use the corresponding features):

ML-NAMD with AIQM1/MRCI

备注

Please refer to the tutorials of how to use ML for NAMD with MS-ANI and do active learning. The tutorial shown here will need some installations required to perform AIQM1 calculations, while NAMD with ML models will need minimum installations and can be performed online.

Here we show how to use AIQM1/MRCI in propagating LZBL surface-hopping dynamics. Please download the tutorial files (namd_aiqm1.zip).

以下脚本展示了所有步骤：

  • 优化几何构型

  • 运行频率计算

  • 使用Newton-X的例程从Wigner分布中生成初始条件。该示例可以扩展以支持按激发能量窗口进行筛选（有关更多详情，请参阅 手册 ）。

  • 并行传播多条轨迹（此处为16条轨迹，每条轨迹为5 fs，时间步长为0.1 fs）。

  • 将轨迹储存为h5md格式

  • 通过生成群体图来分析结果。

脚本如下：

import mlatom as ml

# Load the initial geometry of a molecule
mol = ml.data.molecule()
mol.charge=1
mol.read_from_xyz_file('cnh4+.xyz')

# Define methods
# .. for NAMD
aiqm1 = ml.models.methods(method='AIQM1',
                        qm_program_kwargs={'save_files_in_current_directory': True,
                                            'read_keywords_from_file':'../materials/mndokw'})
# .. for optimization, frequencies and normal mode calculations
method_optfreq = ml.models.methods(method='B3LYP/Def2SVP', program='pyscf')

# Optimize geometry
geomopt = ml.simulations.optimize_geometry(model=method_optfreq,
                                        initial_molecule=mol)
eqmol = geomopt.optimized_molecule
eqmol.write_file_with_xyz_coordinates('eq.xyz')

# Get frequencies
ml.simulations.freq(model=method_optfreq,
                    molecule=eqmol)
eqmol.dump(filename='eqmol.json', format='json')

# Get initial conditions
init_cond_db = ml.generate_initial_conditions(molecule=eqmol,
                                    generation_method='wigner',
                                    number_of_initial_conditions=16,
                                    initial_temperature=0,
                                    random_seed=1) # To ensure we always get the same initial conditions (should not be used in actual calculations)
init_cond_db.dump('test.json','json')

# 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, # Numbering of states starts from 0!
            'random_seed': 1 # To ensure we always get the same initial conditions (should not be used in actual calculations)
            }

# .. 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')

这是最终的群体图（由于初始条件和跃迁中的随机种子不同，您的图可能会有所不同）：

_images/cnh4%2B_aiqm1cis_lznamd_population.png

您还将获得带有群体信息的文本文件 pop.txt ，其内容应如下：

0.000 0.0 0.0 1.0
0.250 0.0 0.0 1.0
0.500 0.0 0.0 1.0
0.750 0.0 0.0 1.0
...

下载完整文件 cnh4+_aiqm1cis_lznamd_population.txt

Multi-state ANI models

Multi-state learning model (MS-ANI) that has unrivaled accuracy for excited state properties (accuracy is often better than for models targeting only ground state!). We demonstrate that this model can be used for trajectory-surface hopping of multiple molecules (not just for a single molecule!) in:

  • Mikołaj Martyka, Lina Zhang, Fuchun Ge, Yi-Fan Hou, Joanna Jankowska, Mario Barbatti, Pavlo O. Dral. Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning. 2024. Preprint on ChemRxiv: https://doi.org/10.26434/chemrxiv-2024-dtc1w.

Zip with tutorial materials including Jupyter notebook:

msani

ML-NAMD with single-state ML models

In this tutorial, we show an example of running surface-hopping MD with single-state ML models. Please see a separate tutorial on machine learning potentials.

See our paper for more details (please also cite it if you use the corresponding features):

您可以从本文 下载 具有所需初始条件和ML模型的Jupyter笔记本。

教程中的计算速度非常快，您应该能够在一分钟内从30条轨迹中获得5 fs的最终群体图，时间步长为0.25 fs。以下是Jupyter笔记本的代码片段：

import mlatom as ml
import os
import numpy as np

# Read initial conditions
init_cond_db = ml.data.molecular_database.load(filename='materials/init_cond_db_for_pyrazine.json', format='json')

# We need to create a class that accepts the specific arguments shown below and saves the calculated electronic state properties in the molecule object
class mlmodels():
    def __init__(self, nstates = 5):
        folder_with_models = 'materials/lz_models'
        self.models = [None for istate in range(nstates)]
        for istate in range(nstates):
            self.models[istate] = [ml.models.ani(model_file=f'{folder_with_models}/ensemble{ii+1}s{istate}.pt') for ii in range(2)]
            for ii in range(2): self.models[istate][ii].nthreads = 1

    def predict(self,
            molecule=None,
            nstates=5,
            current_state=0,
            calculate_energy=True,
            calculate_energy_gradients=True):

        molecule.electronic_states = [molecule.copy() for ii in range(nstates)]

        for istate in range(nstates):
            moltmp = molecule.electronic_states[istate]
            moltmpens = [moltmp.copy() for ii in range(2)]
            for ii in range(2):
                self.models[istate][ii].predict(molecule=moltmpens[ii], calculate_energy = True, calculate_energy_gradients = True)
            moltmp.energy = np.mean([moltmpens[ii].energy for ii in range(2)])
            moltmp.energy_gradients = np.mean([moltmpens[ii].energy_gradients for ii in range(2)], axis=0)

        molecule.energy = molecule.electronic_states[current_state].energy
        molecule.energy_gradients = molecule.electronic_states[current_state].energy_gradients

models = mlmodels()

# Arguments for running NAMD trajectories
timemax = 5 # fs
namd_kwargs = {
            'model': models,
            'time_step': 0.25, # fs
            'maximum_propagation_time': timemax,
            'dump_trajectory_interval': None,
            'hopping_algorithm': 'LZBL',
            'nstates': 5,
            'random_seed': 1, # making sure that the hopping probabilities are the same (should not be used in actual calculations!)
            'rescale_velocity_direction': 'along velocities',
            'reduce_kinetic_energy': False,
            }

# Run 30 trajectories
dyns = ml.simulations.run_in_parallel(molecular_database=init_cond_db[:30], task=ml.namd.surface_hopping_md, task_kwargs=namd_kwargs)
trajs = [d.molecular_trajectory for d in dyns]
ml.namd.analyze_trajs(trajectories=trajs, maximum_propagation_time=timemax)

# Dump the trajectories
for itraj in range(len(trajs)):
    trajs[itraj].dump(filename=f'traj{itraj+1}.json', format='json')

# Prepare the population plot
ml.namd.plot_population(trajectories=trajs, time_step=0.25,
                        max_propagation_time=timemax, nstates=5, filename=f'pop.png')

由于我们使用了固定的随机种子，您应该得到以下最终的群体分布：

_images/pyrazine_lznamd_ml_population.png

Analyzing results

data_analysis.ipynb.

data_analysis