ArticleScientific data2023
Comprehensive exploration of graphically defined reaction spaces.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
27 citing papers in PubMed.
- AIMNet2-rxn: A machine-learned potential for generalized reaction modeling on a millions-of-pathways scale.Science advances · 2026Article
- BOS-TMC Data Set: DFT Properties of 159k Experimentally Characterized Transition Metal Complexes Spanning Multiple Charge and Spin States.Journal of chemical information and modeling · 2026Article
- Flow matching for reaction pathway generation.Nature communications · 2026Article
- Lithium Surface Restructuring and Dendrite-Like Protrusion Formation in Li-S Electrolytes From Reactive Molecular Simulations With Machine-Learned Potentials.Journal of computational chemistry · 2026Article
- Generative flow model on distance geometry for predicting transition states of chemical reactions.Nature communications · 2026Article
- Reactive machine learning potential for accelerating transition state search in organic synthesis.Nature communications · 2026Article
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Rapid Generation of Transition-State Conformer Ensembles via Constrained Distance Geometry.Journal of chemical information and modeling · 2026Article
- ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models.Journal of chemical information and modeling · 2026Article
- Representative Random Sampling of Chemical Space.Journal of chemical theory and computation · 2025Article
- A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials.Scientific data · 2025Article
- GoFlow: efficient transition state geometry prediction with flow matching and E(3)-equivariant neural networks.Digital discovery · 2025Article
- Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states.Digital discovery · 2025Article
- Fast and Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions.JACS Au · 2025Article
- A Deep Learning-Augmented Density Functional Framework for Reaction Modeling with Chemical Accuracy.JACS Au · 2025Article
- Machine learning applications for thermochemical and kinetic property prediction.Reviews in chemical engineering · 2025Review
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
- Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.Journal of chemical information and modeling · 2025Article
- Data Generation for Machine Learning Interatomic Potentials and Beyond.Chemical reviews · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping. For 33,032 reactions, more than one TS was discovered by conformational sampling, allowing conformational errors in TS prediction to be assessed. Data is supplied at the GFN2-xTB and B3LYP-D3/TZVP levels of theory. A subset of reactions were recalculated at the CCSD(T)-F12/cc-pVDZ-F12 and ωB97X-D2/def2-TZVP levels to establish relative errors. The resulting collection of reactions and properties are called the Reaction Graph Depth 1 (RGD1) dataset. RGD1 represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.