Harnessing Machine Learning to Understand and Design Disordered Solids

Disordered solids lack structural order and exhibit complex atomic environments that challenge conventional theoretical and simulation frameworks. This structural complexity makes it difficult to establish quantitative structure–property relationships and to rationally design new amorphous materials. Recent advances in machine learning (ML) offer powerful tools to address these challenges by enabling high‐dimensional representations of disorder, uncovering hidden correlations, and efficiently exploring expansive compositional and configurational spaces. In this review, we survey the rapidly growing interface between ML and the study of disordered solids. We first examine structural representations that connect atomic disorder with ML models, including composition/property descriptors, atomic‐density encodings, graph representations, and topology‐based methods. We then provide an overview of traditional supervised learning, modern deep‐learning architectures, unsupervised approaches, and generative models for accelerating inverse discovery. Explainable ML techniques are highlighted for their ability to relate data‐driven predictions to underlying physical mechanisms. Building on these foundations, we review emerging applications of ML in predicting global and local structure–property relationships and in enabling inverse design of disordered solids with targeted performance. We conclude by outlining key challenges and opportunities, including the need for large multimodal datasets, interpretable and transferable models, and generative frameworks capable of producing physically meaningful atomic configurations. Collectively, these developments illustrate how ML is becoming an essential tool for understanding and designing disordered solids.