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Topic: AI/ML for Materials Discovery and Computing

Guest Editor

Prof. Ming Hu
Department of Mechanical Engineering, University of South Carolina, Columbia, USA.
E-mail: hu@sc.edu

Special Issue Introduction

The advent of machine learning (ML) and artificial intelligence (AI) has
revolutionized many aspects of modern science and technology and has sparked
significant interest in the material science community in recent years. Despite some
early deployment of AI/ML in materials science and engineering, the full potential
of AI still needs to be explored. This Special Issue highlights the recent progress
of advanced and novel AI/ML algorithms and AI/ML-enhanced computational
approaches, with applications spanning materials science and engineering. These
include fast and accurate material property prediction, crystal structure prediction
(CSP) and generation, material process optimization, high-throughput materials
computing and discovery, and inverse design of novel materials with target or desired
properties.

Topics of interest include, but are not limited to:
● Physics- and chemistry-informed, explainable ML for material development;
● High-throughput material simulation enabled by ML algorithms;
● User-inspired or universal ML interatomic potentials;
● Generative models for CSP;
● AI/ML-accelerated density functional theory and quantum physics and chemistry
methods;
● ML for processing-structure-property-performance (PSPP) relationships of
materials science and engineering;
● AI/ML-guided materials design and characterization;
● Inverse design of novel materials;
● Foundation or large language models for materials development.