Integrating DFT and Machine Learning for Mechanistic Elucidation and Catalyst Optimization in DAP-Catalyzed Asymmetric Aza-Mislow-Evans Reactions.

We report a data-driven framework that integrates density functional theory (DFT) with machine learning (ML) to elucidate the reaction mechanism and guide catalyst optimization in the DAP-catalyzed reductive asymmetric aza-Mislow-Evans reaction. DFT calculations reveal a five-step catalytic cycle comprising hydride generation (DAP-H), conjugate addition to the acrylamide substrate, [2,3]-σ rearrangement to form a boron enolate, reductive cleavage of the sulfenate ester with catalyst turnover, and hydrolysis/tautomerization to deliver the chiral α-hydroxyamide product (R)-2a. The conjugate addition is identified as the rate-determining step, with a calculated free energy barrier of 20.6 kcal/mol. By training an ML model on DFT-derived descriptors, we identified heterolytic P-H bond dissociation energy, entropy, and the LUMO energy as the key predictors of reactivity. Guided by these features, we designed a series of new DAP catalysts (DAP-H-x, x = a-e) that are predicted to lower the rate-determining barrier (16.2-19.6 kcal/mol) while achieving near-perfect enantioselectivity (ee > 99.9%). This study demonstrates a generalizable DFT-ML strategy that not only deepens mechanistic understanding but also accelerates the rational development of asymmetric main-group catalysis.