ArticleChemical science2021
TSNet: predicting transition state structures with tensor field networks and transfer learning.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 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
- AIQM2: organic reaction simulations beyond DFT.Chemical science · 2025Article
- Data Generation for Machine Learning Interatomic Potentials and Beyond.Chemical reviews · 2024Review
- Article
- Reinforcement Learning for Traversing Chemical Structure Space: Optimizing Transition States and Minimum Energy Paths of Molecules.The journal of physical chemistry letters · 2024Article
- Diffusion-based generative AI for exploring transition states from 2D molecular graphs.Nature communications · 2024Article
- Reformulating Reactivity Design for Data-Efficient Machine Learning.ACS catalysis · 2023Article
- Comprehensive exploration of graphically defined reaction spaces.Scientific data · 2023Article
- Prediction of transition state structures of gas-phase chemical reactions via machine learning.Nature communications · 2023Article
- Extending machine learning beyond interatomic potentials for predicting molecular properties.Nature reviews. Chemistry · 2022Review
- Low-cost prediction of molecular and transition state partition functionsChemical science · 2022Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Transition states are among the most important molecular structures in chemistry, critical to a variety of fields such as reaction kinetics, catalyst design, and the study of protein function. However, transition states are very unstable, typically only existing on the order of femtoseconds. The transient nature of these structures makes them incredibly difficult to study, thus chemists often turn to simulation. Unfortunately, computer simulation of transition states is also challenging, as they are first-order saddle points on highly dimensional mathematical surfaces. Locating these points is resource intensive and unreliable, resulting in methods which can take very long to converge. Machine learning, a relatively novel class of algorithm, has led to radical changes in several fields of computation, including computer vision and natural language processing due to its aptitude for highly accurate function approximation. While machine learning has been widely adopted throughout computational chemistry as a lightweight alternative to costly quantum mechanical calculations, little research has been pursued which utilizes machine learning for transition state structure optimization. In this paper TSNet is presented, a new end-to-end Siamese message-passing neural network based on tensor field networks shown to be capable of predicting transition state geometries. Also presented is a small dataset of S
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