title: Fast and accurate modeling of molecular atomization energies with 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 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.
authors.affiliation: Machine Learning Group, Technical University of Berlin, Franklinstr 28/29, 10587 Berlin, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Rupp
authors.forename: Alexandre
authors.initials: A
authors.lastname: Tkatchenko
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.forename: O Anatole
authors.initials: OA
authors.lastname: von Lilienfeld