Adaptive representation of molecules and materials in Bayesian optimization.

ABSTRACT: Bayesian optimization (BO) is increasingly used in molecular optimization and in guiding self-driving laboratories for automated materials discovery. A crucial aspect of BO is how molecules and materials are represented as feature vectors, where both the completeness and compactness of these representations can influence the efficiency of the optimization process. Traditionally, a fixed representation is chosen by expert chemists or applying data-driven feature selection methods on available labeled datasets. However, when dealing with novel optimization tasks, prior knowledge or large datasets are often unavailable, and relying on these even can introduce bias into the search process. In this work, we demonstrate a Feature Adaptive Bayesian Optimization (FABO) framework, which integrates

SUBMITTER: Rajabi-Kochi M 

PROVIDER: S-EPMC11851078 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature