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• W2104489082 abstract "We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrodinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross-validation over more than seven thousand small organic molecules yields a mean absolute error of ~10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves." @default.
• W2104489082 created "2016-06-24" @default.
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• W2104489082 date "2012-01-31" @default.
• W2104489082 modified "2023-10-15" @default.
• W2104489082 title "Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning" @default.
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• W2104489082 doi "https://doi.org/10.1103/physrevlett.108.058301" @default.
• W2104489082 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/22400967" @default.
• W2104489082 hasPublicationYear "2012" @default.
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