MAISE：神经网络原子间模型的构建和进化结构优化
MAISE: Construction of neural network interatomic models and evolutionary structure optimization
摘要：
Module for ab initio structure evolution（MAISE）是一个用于材料建模和预测的开源软件包。该代码的主要功能是自动生成神经网络（NN）原子间势，用于全局结构搜索。Behler-Parrinello型NN模型的系统构建近似从头算能量和力依赖于我们最近的研究中介绍的两种方法。用于生成参考结构的进化采样方案改进了NN在无约束搜索中访问的区域的映射，而分层训练方法使得能够为多个元素创建标准化NN模型。一个更灵活的NN架构，这里提出的分层方案的适用性扩展为任意数量的元素。NN开发中的完整工作流程由Python编写的可定制的“MAISE-NET”包装器管理。MAISE的全局结构优化能力基于适用于纳米颗粒、薄膜和块状晶体的进化算法。该算法的多部落扩展允许在给定的尺寸范围内的纳米粒子的有效的同时优化。实现的结构分析功能包括指纹与径向分布函数和寻找空间群与SPGLIB工具。这项工作概述了MAISE的可用功能，构建的模型，并确认的预测。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
