Journal Article

PyXtal_FF: a python library for automated force field generation

Tang, Binh, Zhu, Qiang, Yanxon, Howard, Zagaceta, David, Matteson, David S

2021

Overview

We present PyXtal_FF-a package based on Python programming language-for developing machine learning potentials (MLPs). The aim of PyXtal_FF is to promote the application of atomistic simulations through providing several choices of atom-centered descriptors and machine learning regressions in one platform. Based on the given choice of descriptors (including the atom-centered symmetry functions, embedded atom density, SO4 bispectrum, and smooth SO3 power spectrum), PyXtal_FF can train MLPs with either generalized linear regression or neural network models, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from ab-initio simulations. The trained MLP model from PyXtal_FF is interfaced with the Atomic Simulation Environment (ASE) package, which allows different types of light-weight simulations such as geometry optimization, molecular dynamics simulation, and physical properties prediction. Finally, we will illustrate the performance of PyXtal_FF by applying it to investigate several material systems, including the bulk SiO2, high entropy alloy NbMoTaW, and elemental Pt for general purposes. Full documentation of PyXtal_FF is available at https://pyxtal-ff.readthedocs.io.

• Publisher: IOP Publishing
• Subject: Alloying elements / atom-centered descriptors / Atomic properties / atomistic simulation / High entropy alloys / Machine learning / machine learning potential / Model testing / Molecular dynamics / Neural networks / neural networks regression / Optimization / Physical properties / Programming languages / Python / Silicon dioxide / Simulation / Stress tensors / Sulfur trioxide / Tensors / Weight reduction