Data-driven approach has emerged as a powerful strategy in the construction of structure-performance relationships in organic synthesis.To close the gap between mechanistic understanding and synthetic prediction,we ha...Data-driven approach has emerged as a powerful strategy in the construction of structure-performance relationships in organic synthesis.To close the gap between mechanistic understanding and synthetic prediction,we have made efforts to implement mechanistic knowledge in machine learning modelling of organic transformation,as a way to achieve accurate predictions of reactivity,regio-and stereoselectivity.We have constructed a comprehensive and balanced computational database for target radical transformations(arene C—H functionalization and HAT reaction),which laid the foundation for the reactivity and selectivity prediction.Furthermore,we found that the combination of computational statistics and physical organic descriptors offers a practical solution to build machine learning structure-performance models for reactivity and regioselectivity.To allow machine learning modelling of stereoselectivity,a structured database of asymmetric hydrogenation of olefins was built,and we designed a chemical heuristics-based hierarchical learning approach to effectively use the big data in the early stage of catalysis screening.Our studies reflect a tiny portion of the exciting developments of machine learning in organic chemistry.The synergy between mechanistic knowledge and machine learning will continue to generate a strong momentum to push the limit of reaction performance prediction in organic chemistry.展开更多
基金support fromthe National Natural Science Foundation of China(21873081and 22122109,X.H.,22103070,S.-Q.Z.)the Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study(SN-ZJU-SIAS-006,X.H.)+3 种基金Beijing National Laboratory for Molecular Sciences(BNLMS202102,X.H.)the Centerof Chemistry for Frontier Technologies and Key Laboratory of Precise Synthesis of Functional Molecules of Zhejiang Province(PSFM 2021-01,X.H.)the State Key Laboratory of Clean Energy Utilization(ZJUCEU2020007,X.H.)CAS Youth Interdisciplinary Team(JCTD-2021-11,X.H.)。
文摘Data-driven approach has emerged as a powerful strategy in the construction of structure-performance relationships in organic synthesis.To close the gap between mechanistic understanding and synthetic prediction,we have made efforts to implement mechanistic knowledge in machine learning modelling of organic transformation,as a way to achieve accurate predictions of reactivity,regio-and stereoselectivity.We have constructed a comprehensive and balanced computational database for target radical transformations(arene C—H functionalization and HAT reaction),which laid the foundation for the reactivity and selectivity prediction.Furthermore,we found that the combination of computational statistics and physical organic descriptors offers a practical solution to build machine learning structure-performance models for reactivity and regioselectivity.To allow machine learning modelling of stereoselectivity,a structured database of asymmetric hydrogenation of olefins was built,and we designed a chemical heuristics-based hierarchical learning approach to effectively use the big data in the early stage of catalysis screening.Our studies reflect a tiny portion of the exciting developments of machine learning in organic chemistry.The synergy between mechanistic knowledge and machine learning will continue to generate a strong momentum to push the limit of reaction performance prediction in organic chemistry.
