Single molecule: Production simulation with retrained ML potential¶

#!/usr/bin/env amspython
# coding: utf-8

# ## Initial imports and load active learning job from disk

from scm.simple_active_learning import SimpleActiveLearningJob
import scm.plams as plams
import os
import matplotlib.pyplot as plt


plams.init()


# replace the path with your own path !
previous_sal_job_path = os.path.expandvars("$AMSHOME/examples/SAL/Output/SingleMolecule/plams_workdir/sal")
sal_job = SimpleActiveLearningJob.load_external(previous_sal_job_path)
params_job = sal_job.results.get_params_job()


# ## Structure for production job
#
# We could initialize a PLAMS molecule in many different ways. Here, we get the final frame from the final production simulation in the SAL job, and preoptimize it with UFF.

molecule = sal_job.results.get_main_molecule()
molecule = plams.preoptimize(molecule, model="UFF")
plams.plot_molecule(molecule)


# ## Settings for production job
#
# Let's run a geometry optimization + frequencies. But you could run any AMS job!
#
# The ``MaxRestarts`` option is useful when calculating normal modes. If the geometry optimizer converges to a transition state, it will continue until it finds a local minimum!

s = plams.Settings()
s.input.ams.Task = "GeometryOptimization"
s.input.ams.Properties.NormalModes = "Yes"
s.input.ams.GeometryOptimization.MaxRestarts = 5
s.runscript.nproc = 1

retrained_engine_settings = params_job.results.get_production_engine_settings()

new_job = plams.AMSJob(settings=s + retrained_engine_settings, name="retrained_m3gnet", molecule=molecule)
print(new_job.get_input())


new_job.run()


# ### Optimized structure

plams.plot_molecule(new_job.results.get_main_molecule())


# ### Frequencies

width = 50  # cm^-1
x, y = new_job.results.get_ir_spectrum(broadening_width=width, post_process="all_intensities_to_1")
plt.plot(x, y, label="Retrained M3GNet")
plt.xlabel("Frequency (cm^-1)")
plt.ylabel("Normal mode count")
plt.legend()


# ## Compare to reference UFF
# In this case, we can also calculate the normal modes with the reference method (UFF) and compare:

ref_engine_settings = plams.Settings()
ref_engine_settings.input.ForceField.Type = "UFF"
ref_job = plams.AMSJob(settings=s + ref_engine_settings, name="uff_ref", molecule=molecule)
ref_job.run()


retrained_structure_rmsd = plams.Molecule.rmsd(
    ref_job.results.get_main_molecule(),
    new_job.results.get_main_molecule(),
    ignore_hydrogen=True,
)
print(f"Structural RMSD: {retrained_structure_rmsd:.2f} angstrom")


x_ref, y_ref = ref_job.results.get_ir_spectrum(broadening_width=width, post_process="all_intensities_to_1")
plt.plot(x, y, label="Retrained M3GNet")
plt.plot(x_ref, y_ref, label="UFF (reference method)")
plt.xlabel("Frequency (cm^-1)")
plt.ylabel("Normal mode count")
plt.legend()


# The agreement looks very good! The only significant difference is the highest-frequency vibration (the O-H streching vibration). This frequency is very sensitive to the calculated forces near the equilibrium (minimum) structure. The agreement could have been improved by
#
# * having more training data, for example by setting a tighter success criterion for the forces and energy in the active learning
# * running the active learning MD at a lower temperature (closer to the equilibrium structure, but this would mean less conformational sampling)
# * training for more epochs
#
# Tip: check if the vibrational frequencies with retrained M3GNet or M3GNet-UP-2022 agree better or worse with the reference UFF calculation.