Automated potential development workflow: Application to BaZrO3

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. Program title: Automated potential development (APD) workflow CPC Library link to program files: https://doi.org/10.17632/pd9gbxyy8d.1 Developer's repository link: https://gitlab.com/AndyDuff123/meamfit Licensing provisions: BSD 3-clause Programming language: Python, Bash Nature of problem: Development of new interatomic potentials is a time-consuming process and requires expertise in density functional theory (DFT), potential optimization, and molecular dynamics (and statics) simulations using the optimized potential. Furthermore repeated cycles are usually required to fix numerical issues and improve the potential, requiring further DFT calculations and potential reoptimization. There are also many tedious steps including setting up inputs, submitting jobs (including continuation jobs where jobs terminated prematurely), analyzing outputs, and validating predictions of the interatomic potential. The latter requires multiple calculations of properties computed at both the DFT and potential level. Solution method: The APD workflow provides a black-box solution to fully automate the generation of interatomic potentials. The workflow takes as input a Materials Project id and temperature range of interest. All DFT, potential optimization, and potential molecular dynamics simulations are automatically set-up, submitted and analyzed. Active-learning is incorporated, and properties are computed both at the DFT and potential-level for validation purposes, with tables and graphs for relaxed geometry, elastic constants, phonon dispersion, partial radial distribution functions, and thermal expansion automatically generated. Additional comments including restrictions and unusual features: A full guide to installation, including setting up an environment with all relevant packages and libraries, are included in the distribution. The APD workflow is currently restricted to RF-MEAM potentials and the properties listed above. Some materials which are particularly challenging at the DFT level, e.g. complex magnetic structures, may require intervention in tuning DFT input parameters.