Prediction of Transition State Structures of General Chemical Reactions via Machine Learning

Abstract

The elucidation of transition state (TS) structures is ssential for understanding the mechanisms of chemical reactions and exploring reaction networks. Despite advances in computational approaches, TS searches remain still a challenging problem due to the difficulty of constructing an initial structure and heavy computational costs. Herein, a novel machine learning (ML) model for predicting TS structures of general organic reactions is proposed. The proposed model derives interatomic distances of a TS structure from atomic pair features reflecting reactant, product, and linearly interpolated structures. The model shows excellent accuracy, particularly for the atomic pairs where bond formation or breakage occurs. The predicted TS structures result in a high success ratio (93.8%) of quantum chemical saddle-point optimizations, and 88.8% of the optimization results have energy errors of less than 0.1 kcal/mol. Additionally, as a proof-of-concept, exploring multiple reaction paths of an organic reaction is demonstrated with the ML inferences. I envision that the proposed approach will aid the construction of initial geometry for TS optimization and reaction path explorations.

Comment on ‘Physics-based representations for machine learning properties of chemical reactions’

“…We analyze the performance of predicting barrier heights from the DFT dataset created by Grambow et al [5], which has been used in several other modeling papers [10,16,17,[155][156][157][158]. van Gerwen et al refer to this dataset as GDB7-20-TS.…”

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“…Thirdly, existing methods mainly focus on the generation of low-energy conformers due to their stability. However, exploring molecular conformers in high-energy transition states (TS) is equally significant, as they are pivotal to the progress of chemical reactions [Choi 2023;Duan et al 2023a]. Hence, future research could also concentrate on generating the TS structures for reactants and products, facilitating an enhanced understanding of the kinetics and mechanisms of chemical reactions.…”

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“…More complex models consider other potential energy surface (PES) complexities, such as solvent reorganization in Marcus theory. [17][18][19][20][21] While these models work well for a narrow chemical space and are chemically interpretable, they need to be re-parameterized for even the slightest change to the chemistry and conditions, such as for example, different ligands around a xed reaction centre.…”