  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.
