Exploring reaction dynamics involving post-transition state bifurcations based on quantum mechanical ambimodal transition states
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Ching Ching Lam
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Kendall N. Houk
Abstract
Computational methods for predicting product ratios in dynamically controlled reactions with shallow intermediates or bifurcating pathways after an ambimodal transition state are reviewed and benchmarked. The range of methods includes molecular dynamics simulations, machine learning-based models and recent advancements in correlational methods, all of which rely on quantum mechanical computations. Together, these approaches form a computational toolbox that enhances the efficiency and effectiveness of exploring reaction selectivity influenced by dynamic effects.
Funding source: National Science Foundation
Award Identifier / Grant number: CHE-2153972
Funding source: Croucher Foundation
Award Identifier / Grant number: Croucher Postdoctoral Fellowship
Acknowledgments
C.C.L. thanks Croucher Foundation and the Croucher Postdoctoral Fellowship program for the financial support of this project. K.N.H. thanks the National Science Foundation (CHE2153972) for financial support. This work used computational and storage services associated with the Hoffman2 Shared Cluster provided by the UCLA Institute for Digital Research and Education’s Research Technology Group.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. C. C. Lam performed the research and wrote the paper with the guidance of Prof. K. N. Houk. Prof. K. N. Houk conceptualized and supervised the project.
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Use of Large Language Models, AI and Machine Learning Tools: ChatGPT for checking grammar and polishing text.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: Croucher Postdoctoral Fellowship from Croucher Foundation National Science Foundation (CHE-2153972).
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Data availability: All the data for this paper is available on GitHub: github.com/chingchinglam71/bifucating_product_ratio_prediction.
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