ArticleNature communications2023
Prediction of transition state structures of gas-phase chemical reactions via machine learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Automated transition state generation for mechanistic exploration in organic synthesis.Nature communications · 2026Article
- Generative flow model on distance geometry for predicting transition states of chemical reactions.Nature communications · 2026Article
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 2026Article
- Rapid Generation of Transition-State Conformer Ensembles via Constrained Distance Geometry.Journal of chemical information and modeling · 2026Article
- Adaptive Transition-State Refinement with Learned Equilibrium Flows.Journal of chemical information and modeling · 2026Article
- Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials.Digital discovery · 2025Article
- Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- 3DReact: Geometric Deep Learning for Chemical Reactions.Journal of chemical information and modeling · 2024Article
- Article
- Reinforcement Learning for Traversing Chemical Structure Space: Optimizing Transition States and Minimum Energy Paths of Molecules.The journal of physical chemistry letters · 2024Article
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Authors and funding
1 author.
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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 (93.8%) for quantum chemical saddle point optimizations, and 88.8% of the optimization results have energy errors of less than 0.1 kcal mol
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