Functional monomer design for synthetically accessible polymers.

ABSTRACT: Machine learning (ML) has emerged as a powerful tool to navigate polymer structure-property relationships. Despite recent progress, data sparsity is a major obstacle hindering the practical application of ML in polymer science. In this work, we explore functional monomer design by developing the first comprehensive database of monomer-level chemical and physical properties for approximately 12M synthetically accessible polymers. We generated diverse monomer-level properties by integrating quantum chemistry calculations with active learning to efficiently probe a vast chemical space of synthetically feasible polymers. Monomer-level property descriptors are benchmarked against both higher level computational predictions and experimental data to the extent possible, demonstrating their releva

SUBMITTER: Kim S 

PROVIDER: S-EPMC11823054 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature