Evaluates machine learning predictions for crystal structure outcomes, suggesting practical applications in materials science.
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...
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Shiryaev et al. (2026) studied this question.
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