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.

我们介绍了PyXtal_FF（一种基于Python编程语言的软件包），用于开发潜在的机器学习（MLP）。PyXtal_FF的目的是通过在一个平台上提供多种以原子为中心的描述符和机器学习回归来促进原子模拟的应用。根据给定的描述符选择（包括以原子为中心的对称函数，嵌入的原子密度，SO4双谱和平滑的SO3功率谱），PyXtal_FF可以通过同时使误差最小化来训练具有广义线性回归或神经网络模型的MLP。能量/力/应力张量与从头算得到的数据相比模拟。来自PyXtal_FF的经过训练的MLP模型与“原子模拟环境”（ASE）程序包连接，该程序包允许进行不同类型的轻量级模拟，例如几何优化，分子动力学模拟和物理性质预测。最后，我们将通过应用PyXtal_FF来研究几种材料系统，包括块状SiO ₂，高熵合金NbMoTaW和用于一般用途的元素Pt ，来说明其性能。有关PyXtal_FF的完整文档，请访问https://pyxtal-ff.readthedocs.io。