  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 by providing several choices
of structural descriptors and machine learning regressions in one platform.
Based on the given choice of structural descriptors (including the
atom-centered symmetry functions, embedded atom density, SO4 bispectrum, and
smooth SO3 power spectrum), PyXtal FF can train the MLPs with either the
generalized 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. 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.
