Prediction of transition state structures of gas-phase chemical reactions via machine learning.

ABSTRACT: The elucidation of transition state (TS) structures is essential for understanding the mechanisms of chemical reactions and exploring reaction networks. Despite significant advances in computational approaches, TS searching remains a challenging problem owing to the difficulty of constructing an initial structure and heavy computational costs. In this paper, a machine learning (ML) model for predicting the TS structures of general organic reactions is proposed. The proposed model derives the interatomic distances of a TS structure from atomic pair features reflecting reactant, product, and linearly interpolated structures. The model exhibits excellent accuracy, particularly for atomic pairs in which bond formation or breakage occurs. The predicted TS structures yield a high success ratio (

SUBMITTER: Choi S 

PROVIDER: S-EPMC9977841 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature