Organic Reaction Mechanisms and Reactivity
Our research extends beyond static models by utilizing ab initio molecular dynamics (AIMD) simulations to capture the dynamic behavior of chemical systems in solution. We model explicit solvent effects on reaction mechanisms and stereoselectivity. We integrate mechanistic insights with data-driven approaches into hybrid workflows to enable rapid reactivity and selectivity prediction.
Research Interests
Our research aims to utilize novel computational mehods to understand and predict organic reaction mechanisms and reactivity.
Explicit solvent effects
Understanding the effects of solvent has been a long-standing challenge for computational studies of organic reaction mechanisms and reactivity. Our group utilize ab initio molecular dynamics (AIMD) simulations in the presence of real solvent molecules to study solvent effects on the mechanisms and stereoselectivity of organic reactions.
Representative Publications:
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AIMD metadynamics simulations revealed solvent effects on the concertedness of glycosylation pathways
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JACS 2021, 143, 1577–1589. DOI: 10.1021/jacs.0c12096
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AIMD simulations in solution on an unusual concerted SNV nucleophilic substitution at an sp2-carbon
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Nature, 2024, 631, 328–334. DOI: 10.1038/s41586-024-07579-7
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MD simulations revealed surprising HFIP solvent effects on enantioselectivity
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JACS 2025, 147, 14694–14704. DOI: 10.1021/jacs.5c03007
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Mechanistically informed predictive models
Our group utilizes mechanisic insights from our computational studies to help develop predictive models for reactivity and selectivity. Our quantum mechanical calculations and mechanistic studies provide expert-crafted, physically meaningful descriptors that enhance machine learning model performance, even when only limited data points are available. We have applied these mechanistically guided data-driven appoaches to predicting reactivity of C–H functionalization and ligand effects in transition metal catalysis.
Representative Publications:
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Predicting reactivity of DDQ-mediated C–H functionalization
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JACS 2017, 139, 17935–17944. DOI: 10.1021/jacs.7b08902
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Predictive models for ligand and initiator effects on the reactivity of Cu-catalyzed ATRP
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JACS 2019, 141, 7486–7497. DOI: 10.1021/jacs.9b02158
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Predicting effective electrophiles in multicomponent coupling
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Chem 2020, 6, 2810–2825. DOI: 10.1016/j.chempr.2020.09.004
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HArD: a heteroaryl substituent descriotor database
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Sci. Data 2025, 12, 1319. DOI: 10.1038/s41597-025-05198-z
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