Publikation: Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
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Physical Review Letters. American Physical Society (APS). 2012, 108(5), 058301. ISSN 0031-9007. eISSN 1079-7114. Available under: doi: 10.1103/PhysRevLett.108.058301
Zusammenfassung
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 Schrödinger equation is mapped onto a nonlinear 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 organic molecules yields a mean absolute error of ∼10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.
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RUPP, Matthias, Alexandre TKATCHENKO, Klaus-Robert MÜLLER, O. Anatole VON LILIENFELD, 2012. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning. In: Physical Review Letters. American Physical Society (APS). 2012, 108(5), 058301. ISSN 0031-9007. eISSN 1079-7114. Available under: doi: 10.1103/PhysRevLett.108.058301BibTex
@article{Rupp2012-02-03Accur-52138,
year={2012},
doi={10.1103/PhysRevLett.108.058301},
title={Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning},
number={5},
volume={108},
issn={0031-9007},
journal={Physical Review Letters},
author={Rupp, Matthias and Tkatchenko, Alexandre and Müller, Klaus-Robert and von Lilienfeld, O. Anatole},
note={Article Number: 058301}
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<dcterms:abstract xml:lang="eng">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 Schrödinger equation is mapped onto a nonlinear 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 organic molecules yields a mean absolute error of ∼10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.</dcterms:abstract>
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