Policy-Based Active Learning for Efficient Molecular Identification

settings, the framework demonstrates improved efficiency in identifying optimal molecules within limited evaluation cycles. Case studies further illustrate the strengths and limitations of the framework. These results highlight the potential of policy-driven active learning to enhance molecular identification efficiency in predefined candidate pools, offering a generalizable strategy for budget-constrained chemical discovery.