Description

In order to train machine-learned potentials such as sGDML, one has to generate large amounts of training data. Given a sequence of geometries, this set of scripts distributes QChem jobs over a SLURM queue and stores the forces and NAC vectors in the format expected by sGDML.

Requirements

• QChem
• SLURM queue

Installation

$ pip install -e .

Getting Started

In 'examples/formaldehyde', run

$ sbatch --job-name='create dataset' << EOF
  #!/bin/bash
     forces_trajectory.py   geometries.xyz  qchem.in  --parallel_images=10
  EOF

After some time, this should produce the following files in extended XYZ format (using atomic units):

• forces_0.xyz - forces in S0
• forces_1.xyz - forces in S1
• nacvec_0-1.xyz - NAC vectors between S0 and S1

Geometries at which the forces are calculated, can be sampled from the Maxwell-Boltzmann distribution by running molecular dynamics at high temperature with the ANI-2x force field.

$ ani2x_dynamics.py  initial.xyz --temperature=500.0  -o geometries.xyz