Surface-hopping dynamics

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

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

• 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. J. Chem. Theory Comput. 2024, 20, 5043–5057. DOI: 10.1021/acs.jctc.4c00468. Preprint on arXiv: https://arxiv.org/abs/2404.06189.

Note

Please refer to the tutorials of how to use ML for NAMD with ML models and perform 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).

The script below does all the steps from start to finish:

• optimizes geometry

• runs frequency calculations

• samples initial conditions from the Wigner distribution using Newton-X’s routines. Example can be extended to supports filtering by excitation energy window (refer to the manual for more details)

• propagates multiple trajectories in parallel (here 16 trajectories for 5 fs with 0.1 fs time step)

• saves trajectories in the h5md format

• analyzes results by generating the population plots.

Here is the script:

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')

And here is the final population plot (your plot will be different because of the random seed in initial conditions and hoppings):

You will also get the text file with populations pop.txt which should look like:

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
...

Download the full file cnh4+_aiqm1cis_lznamd_population.txt.