Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Chemical Physics
2015-05-30 v1 Disordered Systems and Neural Networks
Materials Science
Machine Learning
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 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.
Keywords
Cite
@article{arxiv.1109.2618,
title = {Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning},
author = {Matthias Rupp and Alexandre Tkatchenko and Klaus-Robert Müller and O. Anatole von Lilienfeld},
journal= {arXiv preprint arXiv:1109.2618},
year = {2015}
}