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. Program summary Program Title: MAISE CPC Library link to program files: https://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. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

MAISE is an open-source package for materials modeling and prediction, with the main feature of automatically generating neural network interatomic potentials for global structure searches. The code utilizes evolutionary sampling and stratified training approaches to improve NN mapping accuracy and create standardized models for multiple elements. The customizable 'MAISE-NET' wrapper streamlines the workflow in NN development for efficient structure optimization across various chemical species.