Reformulating Reactivity Design for Data-Efficient Machine Learning.

ABSTRACT: Machine learning (ML) can deliver rapid and accurate reaction barrier predictions for use in rational reactivity design. However, model training requires large data sets of typically thousands or tens of thousands of barriers that are very expensive to obtain computationally or experimentally. Furthermore, bespoke data sets are required for each region of interest in reaction space as models typically struggle to generalize. We have therefore reformulated the ML barrier prediction problem toward a much more data-efficient process: finding a reaction from a prespecified set with a desired target value. Our reformulation enables the rapid selection of reactions with purpose-specific activation barriers, for example, in the design of reactivity and selectivity in synthesis, catalyst design, t

SUBMITTER: Lewis-Atwell T 

PROVIDER: S-EPMC10594582 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature