Achieving a scalable machine learning workflow for crystal structure discovery with experimental validation

Abstract

Machine learning (ML) has become a central component of data-driven materials discovery, yet its practical impact remains heavily dependent on how these predictions are translated into experimentally-realizable outcomes. In this review, we examine ML-guided crystal structure discovery through the lens of recommendations as well as unconstrained generation, to emphasize interpretable workflows embedding chemical intuition, physical constraints, and experimental validation. Surveying standalone ML, hybrid ML-DFT, and machine-learned interatomic potential (MLIP) approaches, we highlight how constrained design spaces, data preprocessing, and validation strategies shape novel discovery success. Drawing on our own experimentally-validated case studies, ranging from supervised to unsupervised learning, as well as recommendation-type explorations, we outline the shift towards interpretable and explainable ML models that guide synthetic decisions, reveal trends that were previously difficult to identify, confirm established patterns, and uncover new ones. Collectively, we highlight the results of interpretable ML, which is more effective when deployed within experimental workflows to bridge learning and chemistry, to enable a reliable discovery pathway to solid state materials.