Atomistic Simulation of Glasses and Amorphous Materials: Challenges and Opportunities for the Next Decade

ABSTRACT

Atomistic simulations have become indispensable tools for understanding glass structure, dynamics, and properties, yet persistent challenges limit their predictive power. This perspective examines three interconnected issues, namely, glass formation procedures, interatomic potential development, and machine learning (ML) applications, which emerged from the 5th International Workshop on Challenges of Atomistic Simulations of Glasses and Amorphous Materials. We identify convergent community priorities for standardized validation protocols and curated benchmark datasets with complete metadata. A systematic pathway forward is provided by a hierarchical validation framework for assessing the structural fidelity, property prediction, and behavioral realism of simulation techniques. Looking ahead, transformative advances are promised by the fusion of classical techniques with ML‐based approaches. Examples include the integration of swap Monte Carlo with ML potentials, leveraging foundation models through transfer or active learning, and fine‐tuning ML potentials with experimental data. Progress depends on community awareness and efforts committed to validating models, creating reproducible protocols, and sustained data sharing.