Abstract 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. Program summary Program Title: MAISE CPC Library link to program files: http://dx.doi.org/10.17632/vfzgt2gnsh.1 Licensing provisions: GNU General Public License v3.0 Programming language: C Nature of problem: Construction of NN interatomic potentials suitable for evolutionary structure searches, molecular dynamics, phonon calculations, and other applications presents a host of challenges ranging from sampling relevant parts of vast configuration spaces to tuning multitudes of NN parameters. Solution method: Evolutionary data generation and modular NN training algorithms featured in the open-source parallelized MAISE package enable automated development of NN models for multiple chemical species. Customizable MAISE-NET wrapper streamlines all stages of the iterative process. Unusual features: NN training stratification allows one to build libraries of reusable models from the bottom up, starting from elements and proceeding to multielement chemical systems. A multitribe evolutionary algorithm improves the efficiency of ground state structure searches by simultaneously optimizing nanoparticles of different sizes and periodically exchanging best motifs between the tribes.

中文翻译：

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MAISE：神经网络原子间模型构建与进化结构优化

Abstract Module for ab initio structure Evolution (MAISE) 是一个用于材料建模和预测的开源包。该代码的主要功能是自动生成用于全局结构搜索的神经网络 (NN) 原子间势。近似从头算能量和力的 Behler-Parrinello 型神经网络模型的系统构建依赖于我们最近研究中引入的两种方法。用于生成参考结构的进化采样方案改进了神经网络在无约束搜索中访问区域的映射，而分层训练方法能够为多个元素创建标准化的神经网络模型。这里提出的更灵活的 NN 架构扩展了分层方案对任意数量元素的适用性。NN 开发中的完整工作流程是通过一个用 Python 编写的可定制的“MAISE-NET”包装器来管理的。MAISE 中的全局结构优化功能基于适用于纳米粒子、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尺寸范围内对纳米粒子进行有效的同时优化。已实现的结构分析功能包括使用径向分布函数进行指纹识别和使用 SPGLIB 工具查找空间群。这项工作概述了 MAISE 的可用功能、构建的模型和确认的预测。程序摘要 程序名称：MAISE CPC 库程序文件链接：http://dx.doi.org/10.17632/vfzgt2gnsh.1 许可条款：GNU 通用公共许可证 v3.0 编程语言：C 问题性质：适用于进化结构搜索、分子动力学、声子计算和其他应用的 NN 原子间势的构建提出了许多挑战，从对巨大配置空间的相关部分进行采样到调整大量 NN 参数。解决方法：开源并行化 MAISE 包中的进化数据生成和模块化 NN 训练算法能够自动开发多种化学物质的 NN 模型。可定制的 MAISE-NET 包装器简化了迭代过程的所有阶段。不寻常的功能：NN 训练分层允许人们自下而上地构建可重复使用的模型库，从元素开始，再到多元素化学系统。