RxnNet: An AI Framework for Reaction Mechanism Discovery─A Case Study of Carbocations (original) (raw)
Reaction MechanismsMarch 8, 2026
RxnNet: An AI Framework for Reaction Mechanism Discovery─A Case Study of Carbocations
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- Shani Zev
Department of Chemistry and Institute for Nanotechnology & Advanced Materials and Israel National Institute for Energy Storage (INIES), Bar-Ilan University, Ramat-Gan 5290002, Israel - Michal Roth
Michal Roth
Department of Physics and Institute for Nanotechnology & Advanced Materials, Bar-Ilan University, Ramat-Gan 5290002, Israel - Jishnu Narayanan S J
Jishnu Narayanan S J
Department of Chemistry and Institute for Nanotechnology & Advanced Materials and Israel National Institute for Energy Storage (INIES), Bar-Ilan University, Ramat-Gan 5290002, Israel - Dan T. Major*
Dan T. Major
Department of Chemistry and Institute for Nanotechnology & Advanced Materials and Israel National Institute for Energy Storage (INIES), Bar-Ilan University, Ramat-Gan 5290002, Israel
*****Email: [email protected]
Journal of Chemical Theory and Computation
Cite this: J. Chem. Theory Comput. 2026, 22, 6
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research-article
Copyright © 2026 American Chemical Society
Abstract
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Understanding complex chemical reaction cascades remains a major challenge in chemistry. Scalable investigation of their thermodynamic and kinetic properties requires the use of automated reaction prediction tools, which is a rapidly growing area in the study of chemical reactivity. However, the systematic exploration of intricate reaction networks involving highly reactive intermediates continues to pose significant difficulties. Here, we introduce RxnNet, a novel artificial intelligence-assisted platform for the automated prediction of chemical reaction mechanisms. RxnNet integrates heuristic rules with domain-specific chemical knowledge including stereochemistry, regiochemistry, conformational preferences, and isotope labeling, to construct mechanistically informed reaction networks. These networks are represented as graphs and are coupled with on-the-fly quantum chemical evaluations to identify all feasible intermediates and transition states. In this work, we apply RxnNet to carbocation chemistry, a notoriously complex and computationally demanding type of reaction. We demonstrate the method’s capabilities by analyzing three multistep reactions with known mechanisms, each of which poses significant challenges even for expert computational and synthetic chemists. RxnNet provides a robust approach for uncovering reaction mechanisms, which can accelerate the understanding and design of transformations in complex chemical systems.
ACS Publications
Copyright © 2026 American Chemical Society
Supporting Information
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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jctc.5c02103.
Tables and figures with results for all test cases. Cartesian coordinates of structures. MEPs of all mechanisms as obtained from RxnNet. Movies of all mechanisms as obtained from RxnNet (PDF)
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Journal of Chemical Theory and Computation
Cite this: J. Chem. Theory Comput. 2026, 22, 6
Click to copy citationCitation copied!
Copyright © 2026 American Chemical Society
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