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

TL;DR

MAISE is an open-source package that automates the creation of neural network interatomic potentials and employs evolutionary algorithms for efficient global structure optimization of materials.

Contribution

The paper introduces new methods for automated neural network potential generation and an extended evolutionary optimization framework for diverse material structures.

Findings

Successfully constructed neural network potentials for multiple elements.

Demonstrated efficient global structure searches for nanoparticles and bulk materials.

Validated predictions with known material structures.

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.