Automated potential development workflow: Application to

We present a Python workflow, Automated Potential Development (APD), for automating the development of interatomic potentials, including calculation of density functional theory (DFT) fitting data, optimization of potentials, and potential-driven molecular dynamics (MD) simulations. The workflow currently supports CASTEP and VASP DFT codes and the MEAMfit potential optimization code for optimization of reference-free modified embedded atom method (RF-MEAM) potentials. The LAMMPS software is supported for calculating the relaxed geometry, elastic constants, phonon dispersion, thermal expansion and radial distribution functions using the optimized potentials. These same properties are also computed at the DFT level and APD automatically generates plots and tables of these data. Query by-committee active learning is supported, using multiple fitted potentials to evaluate the energies of atomic configurations generated from LAMMPS MD runs. The workflow is demonstrated on BaZrO3, an oxide-based perovskite material, with RF-MEAM results found in good agreement with DFT.

Automated summary

This Python workflow automates the development of interatomic potentials using density functional theory (DFT) fitting data calculation, potential optimization, and potential-driven molecular dynamics (MD) simulations. It supports different software tools for computation and optimization, and automatically generates plots and tables of the computed data.