ReLMM: Reinforcement Learning Optimizes Feature Selection in Modeling Materials.

ABSTRACT: A challenge to materials discovery is the identification of the physical features that are most correlated to a given target material property without redundancy. Such variables necessarily comprise the optimal search domain in subsequent material design. Here, we introduce a reinforcement learning-based material model (ReLMM) as a tool for analyzing a given database in identifying a minimal or near minimal subset of physical features for the design of a material with a given target property. We aim for minimality in the selected subset with respect to its size─smaller being better─ while maintaining the desired accuracy of the prediction. We have shown, using synthetic multiscale data sets, that ReLMM can identify the relative importance of features, and thus help identify which should be

SUBMITTER: Sharma Priyadarshini M 

PROVIDER: S-EPMC11734688 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature