People

@article{WOS:001711023100001,
title = {Artificial Intelligence for Multiscale Modeling in Solid-State Physics
and Chemistry: A Comprehensive Review},
author = {Artem Maevskiy and Vitalii Kapitan and Andrey Ustyuzhanin},
doi = {10.1002/aisy.202501219},
times_cited = {0},
year = {2026},
date = {2026-05-01},
journal = {ADVANCED INTELLIGENT SYSTEMS},
volume = {8},
number = {5},
publisher = {WILEY-V C H VERLAG GMBH},
address = {POSTFACH 101161, 69451 WEINHEIM, GERMANY},
abstract = {Recent progress in artificial intelligence (AI) has transformed
methodologies across many areas of science. In materials research, AI
has enabled more efficient multiscale modeling by linking atomic,
mesoscale, and continuum scales with improved accuracy and reduced
computational cost. This review examines AI-based approaches in this
context and discusses their relationship to conventional analytical and
computational multiscale methods. Developments such as machine learning
force fields, graph neural networks, and AI-accelerated electronic
structure prediction are assessed with respect to their capabilities and
limitations. To illustrate the current state of the art in this field,
available software, computational tools, and benchmarks are discussed.
Applications in areas such as phase transitions, defect dynamics, and
bulk property prediction are shown, with an emphasis on how AI enhances
predictive capabilities. While highlighting the above-mentioned recent
advances, existing challenges and promising directions are also
discussed. This review is intended for two audiences: For AI
researchers, it demonstrates how physical and chemical constraints
influence models' development to ensure physical consistency, and for
physicists, chemists, and materials scientists, it illustrates how AI
can improve multiscale methods to solve previously inaccessible problems},
keywords = {},
pubstate = {published},
tppubtype = {article}
}