Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias RuppMachine Learning Group, Technical University of Berlin, Franklinstr 28/29, 10587 Berlin, GermanyAlexandre TkatchenkoFritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, GermanyKlaus‐Robert MüllerInstitute of Pure and Applied Mathematics, University of California Los Angeles, Los Angeles, California 90095, USAO. Anatole von LilienfeldArgonne Leadership Computing Facility, Argonne National Laboratory, Argonne, Illinois 60439, USA
2012en
ABI
Аннотация
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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