Integrating DFT and Machine Learning for Mechanistic Elucidation and Catalyst Optimization in DAP-Catalyzed Asymmetric Aza-Mislow–Evans Reactions
) 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.
Authors
- Lili Zhao (ORCID: https://orcid.org/0000-0003-2580-6919)
- Cheng‐Xing Cui (ORCID: https://orcid.org/0000-0001-9632-5798)
- Wei Zhang (ORCID: https://orcid.org/0000-0002-9756-9994)
- Lei Qin
- Xing Yang
- Chen Zhang
- Yan Zhang
- Xiaohong Liu
- Tianqi Wang
Institutions
- Central South University of Forestry and Technology (CN)
- Institute of Applied Physics (UA)
- Nanjing Tech University (CN)
- National University of Singapore (SG)
- Alberta Energy (CA)
- Henan Academy of Sciences (CN)
- Chongqing Institute of Green and Intelligent Technology (CN)
- Institute of Engineering (NP)
- Computational Physics (United States) (US)
- Institute of Applied Physics and Computational Mathematics (CN)
- Henan Institute of Science and Technology (CN)
- Xinxiang University (CN)
Publication Details
- Journal
- The Journal of Physical Chemistry Letters
- Published
- 2026-04-13
- DOI
- https://doi.org/10.1021/acs.jpclett.6c00638
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Jiangsu Province
- Nanjing Tech University
- State Key Laboratory of Materials- Oriented Chemical Engineering