GapMD¶

GapMD is an efficient way to explore the region near the conical intersections, see for details:

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. npj Comput. Mater. 2025, 11, 132. DOI: 10.1038/s41524-025-01636-z. Preprint on ChemRxiv: https://doi.org/10.26434/chemrxiv-2024-dtc1w.

Zip with tutorial materials including Jupyter notebook:

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import mlatom as ml

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#Load some database of initial conditions.
initial_conditions = ml.data.molecular_database.load("init_cond_db.json", format="json")

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#Define a model, we will use the AL-trained ML model for fulvene.
ml_model = ml.models.msani(model_file='fulvene_energy_iteration_19.npz')

model loaded from fulvene_energy_iteration_19.npz

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#Initialize a gapMD run
dyn = ml.gap_md(
                model = ml_model,
                molecule = initial_conditions[-1],
                ensemble='NVE',
                time_step=0.5,
                maximum_propagation_time = 100,
                lower_state =0,
                upper_state =1,
                current_state=1,
                nstates=2,
                gap_threshold=0.03,
            )

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#View the gap dynamics. Notice the ethylenic-type CI twisting between the 
#5-membered ring and =CH2.
dyn.molecular_trajectory.view()

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

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#GapMD can also be ran in parallel. For that we need to prepare parameters,
#like for NAMD...
gapmd_kwargs ={                
                'model':ml_model,
                'ensemble':'NVE',
                'time_step':0.5,
                'maximum_propagation_time':100,
                'lower_state':0,
                'upper_state':1,
                'current_state':1,
                'nstates':2,
                'gap_threshold':0.03}

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#... and use the same method, run_in_parallel to run them all.
dyns = ml.simulations.run_in_parallel(molecular_database=initial_conditions[:2], task=ml.gap_md, task_kwargs=gapmd_kwargs, create_and_keep_temp_directories=False)

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trajs = [d.molecular_trajectory for d in dyns]
for traj in trajs:
    traj.view()

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#MLatom trajectories can be analyzed and visualized using simple Python code. An example below.
energies = [[],[]]
time = []
gap = []
for step in trajs[0].steps:
    energies[0].append(step.molecule.electronic_states[0].energy)
    energies[1].append(step.molecule.electronic_states[1].energy)
    time.append(step.time)
    gap.append(step.molecule.electronic_states[1].energy-step.molecule.electronic_states[0].energy)

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import matplotlib.pyplot as plt
import numpy as np
plt.plot(time, np.transpose(energies))
plt.xlabel("Time (fs)")
plt.ylabel("Energy (Hartree)")
plt.legend(['S0', 'S1'])

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<matplotlib.legend.Legend at 0x2b520fd35880>

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plt.plot(time, gap)
plt.xlabel("Time (fs)")
plt.ylabel("Gap (Hartree)")

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Text(0, 0.5, 'Gap (Hartree)')

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