Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Details Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

2011-09-12

arxiv.org/abs/1109.2618v1

Physics

Chemical Physics

Scientific paper

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 Schr\"odinger 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.

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