ArticleChemical science2022
Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction.
Article in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- BONAFIDE: a Python framework for the calculation of local features for atoms and bonds in molecules.Journal of cheminformatics · 2026Article
- Chalcogen Impurity Barriers in 2D Systems via Semi-Empirical/Machine Learning Modeling: A Survey of 4000 Materials.Journal of chemical theory and computation · 2026Article
- Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states.Digital discovery · 2025Article
- Quantum chemical properties of chlorinated polycyclic aromatic hydrocarbons for delta machine learning.Scientific data · 2025Article
- Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.Journal of chemical information and modeling · 2025Article
- Geometry-Corrected Quadratic Optimization Algorithm for NDDO-Descendant Semiempirical Models.Journal of chemical theory and computation · 2025Article
- 3DReact: Geometric Deep Learning for Chemical Reactions.Journal of chemical information and modeling · 2024Article
- Benchmarking machine-readable vectors of chemical reactions on computed activation barriers.Digital discovery · 2024Article
- Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates.Chemical science · 2024Article
- Reformulating Reactivity Design for Data-Efficient Machine Learning.ACS catalysis · 2023Article
- Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations.The journal of physical chemistry. A · 2023Article
- Prediction of transition state structures of gas-phase chemical reactions via machine learning.Nature communications · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern QM modelling methods, such as DFT, have provided detailed mechanistic insights into countless reactions. However, their computational cost inhibits their ability to rapidly screen large numbers of substrates and catalysts in reaction discovery. For a C-C bond forming nitro-Michael addition, we introduce a synergistic semi-empirical quantum mechanical (SQM) and machine learning (ML) approach that allows the prediction of DFT-quality reaction barriers in minutes, even on a standard laptop using widely available modelling software. Mean absolute errors (MAEs) are obtained that are below the accepted chemical accuracy threshold of 1 kcal mol
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Registered trials
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