ArticleThe journal of physical chemistry. A2023
Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations.
Article in The journal of physical chemistry. A, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Near Black Box Parameter Optimizer for NDDO-Descendant Semiempirical Methods: Demonstrations for MNDO and AM1.The journal of physical chemistry. A · 2026Article
- Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states.Digital discovery · 2025Article
- AIQM2: organic reaction simulations beyond DFT.Chemical science · 2025Article
- A Deep Learning-Augmented Density Functional Framework for Reaction Modeling with Chemical Accuracy.JACS Au · 2025Article
- Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.Journal of chemical information and modeling · 2025Article
- Finding defects in glasses through machine learning.Nature communications · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Different machine learning (ML) models are proposed in the present work to predict density functional theory-quality barrier heights (BHs) from semiempirical quantum mechanical (SQM) calculations. The ML models include a multitask deep neural network, gradient-boosted trees by means of the XGBoost interface, and Gaussian process regression. The obtained mean absolute errors are similar to those of previous models considering the same number of data points. The ML corrections proposed in this paper could be useful for rapid screening of the large reaction networks that appear in combustion chemistry or in astrochemistry. Finally, our results show that 70% of the features with the highest impact on model output are bespoke predictors. This custom-made set of predictors could be employed by future Δ-ML models to improve the quantitative prediction of other reaction properties.
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Registered trials
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