PyXtal_FF: a python library for automated force field generation
PyXtal_FF: a python library for automated force field generation
复制标题
DOI：
10.1088/2632-2153/abc940
复制
发表时间：
2020-07
期刊：
影响因子：
--
通讯作者：
Howard Yanxon;David Zagaceta;Binh Tang;D. Matteson;Q. Zhu
中科院分区：
文献类型：
--
作者：
Howard Yanxon;David Zagaceta;Binh Tang;D. Matteson;Q. Zhu
提出了一种基于Python语言的开发机器学习潜能的程序包PYXTAL_FF。PYXTAL_FF的目标是通过在一个平台上提供多个以原子为中心的描述符和机器学习回归的选择来促进原子模拟的应用。基于给定的描述符(包括原子中心对称函数、嵌入原子密度、SO4双谱和平滑SO3功率谱)的选择，PYXTAL_FF可以用广义线性回归或神经网络模型训练MLP，同时将能量/力/应力张量的误差与从头计算的数据进行比较。来自PYXTAL_FF的训练好的MLP模型与原子模拟环境(ASE)包接口，该包允许不同类型的轻量级模拟，如几何优化、分子动力学模拟和物理性质预测。最后，我们将通过将其应用于几个材料系统的研究来说明它的性能，这些材料系统包括块体SiO_2、高熵合金NbMoTaW和通用元素铂。有关PYXTAL_FF的完整文档，请访问https://pyxtal-ff.readthedocs.io.
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.