Special Topic: AI for Chemistry
Open Access
Review

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AI-driven catalyst design and optimization. (A) Data-intensive approach to mechanistic elucidation of chiral ionic catalysis: Introduced by F.D. Toste and M.S. Sigman in 2015, this approach entailed a comprehensive analysis of various catalysts and substrates, revealing the key mechanistic attributes of the system and insights into enantioselectivity [68]. Copyright©2015, American Association for the Advancement of Science. (B) Stereodivergent Pd-catalyzed cross-coupling reactions: M.S. Sigman and M.R. Biscoe employed molecular modeling and parameterization in conjunction with experimental validation to elucidate the factors governing stereoselectivity in 2018 [69]. Copyright©2018, American Association for the Advancement of Science. (C) Monodentate phosphine ligands classification: In 2021, M.S. Sigman and A.G. Doyle presented a methodology to categorize monodentate phosphine ligands based on their connectivity status and reactivity, enabling a more nuanced understanding of reaction outcomes in cross-coupling catalysis [71]. Copyright©2021, American Association for the Advancement of Science. (D) Computationally guided catalyst selection: S.E. Denmark demonstrated in 2019 how machine learning and cheminformatics could revolutionize the selection of chiral catalysts [72]. Copyright©2019, American Association for the Advancement of Science. (E) Automatic reactive optimization and parallel scheduling (AROPS): Introduced by Y. Mo in 2023, AROPS effectively integrates optimization algorithms with module scheduling, resulting in a more efficient experimental process [73]. Copyright©2023, American Chemical Society.

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