MAISE: Construction of neural network interatomic models and evolutionary structure optimization

Module for ab initio structure evolution (MAISE) is an open-source package for materials modeling and prediction. The code’s main feature is an automated generation of neural network (NN) interatomic potentials for use in global structure searches. The systematic construction of Behler–Parrinello-type NN models approximating ab initio energy and forces relies on two approaches introduced in our recent studies. An evolutionary sampling scheme for generating reference structures improves the NNs’ mapping of regions visited in unconstrained searches, while a stratified training approach enables the creation of standardized NN models for multiple elements. A more flexible NN architecture proposed here expands the applicability of the stratified scheme for an arbitrary number of elements. The full workflow in the NN development is managed with a customizable ‘MAISE-NET’ wrapper written in Python. The global structure optimization capability in MAISE is based on an evolutionary algorithm applicable for nanoparticles, films, and bulk crystals. A multitribe extension of the algorithm allows for an efficient simultaneous optimization of nanoparticles in a given size range. Implemented structure analysis functions include fingerprinting with radial distribution functions and finding space groups with the SPGLIB tool. This work overviews MAISE’s available features, constructed models, and confirmed predictions.从头算结构演化模块（MAISE）是一款面向材料建模与预测的开源软件包。该软件的核心功能为自动化生成神经网络（NN）原子间势函数，用于全局结构搜索。构建近似从头算能量与受力的贝勒-帕里内洛（Behler–Parrinello）型神经网络模型的系统化流程，依托于我们近期研究中提出的两种方法。用于生成参考结构的演化采样方案，可优化神经网络对无约束搜索中遍历区域的映射能力；而分层训练方法则支持为多元素体系构建标准化的神经网络模型。本文提出的更灵活的神经网络架构，进一步拓展了分层训练方案对任意元素数量体系的适用性。神经网络开发的完整流程，由基于Python编写的可定制化“MAISE-NET”封装工具进行管理。MAISE中的全局结构优化功能，基于适用于纳米颗粒、薄膜与块体晶体的演化算法。该算法的多种群扩展版本，可实现对给定尺寸范围内纳米颗粒的高效并行优化。已实现的结构分析功能包括：利用径向分布函数进行特征指纹化，以及借助SPGLIB工具求解空间群。本文综述了MAISE的可用功能、已构建的模型与已验证的预测结果。