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Introduction
PyXtal FF is an open-source Python library for developing machine learning interatomic potential of materials.
Current Features
The aim of PyXtal_FF is to promote the application of atomistic simulations by providing several choices of structural descriptors and machine learning regressions in one platform. Based on the given choice of structural descriptors including
- atom-centered symmetry functions,
- embedded atom density,
- SO4 bispectrum,
- smooth SO3 power spectrum.
PyXtal_FF can train the MLPs with either the linear regression or neural networks model, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from the ab-initio simulation.
See the documentation page for more details.
Relevant works
[1]. Yanxon H, Zagaceta D, Tang B, Matteson D, Zhu Q (2020)
PyXtal_FF: a Python Library for Automated Force Field Generation (to appear soon)
[2]. Zagaceta D, Yanxon H, Zhu Q (2020)
Spectral Neural Network Potentials for Binary Alloys
[3]. Yanxon H, Zagaceta D, Wood B, Zhu Q (2019)
On Transferability of Machine Learning Force Fields: A Case Study on Silicon
[4]. Fredericks S, Sayre D, Zhu Q (2019)
PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis