Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
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Rupp, Matthias[Machine Learning Group, Technical University of Berlin, Franklinstr 28/29, 10587 Berlin, Germany > > > ; Institute of Pure and Applied Mathematics, University of California Los Angeles, Los Angeles, California 90095, USA]
Tkatchenko, Alexandre[Institute of Pure and Applied Mathematics, University of California Los Angeles, Los Angeles, California 90095, USA > > > ; Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany]
Mueller, Klaus-Robert[Machine Learning Group, Technical University of Berlin, Franklinstr 28/29, 10587 Berlin, Germany > > > ; Institute of Pure and Applied Mathematics, University of California Los Angeles, Los Angeles, California 90095, USA]
von Lilienfeld, O. Anatole[Institute of Pure and Applied Mathematics, University of California Los Angeles, Los Angeles, California 90095, USA > > > ; Argonne Leadership Computing Facility, Argonne National Laboratory, Argonne, Illinois 60439, USA]
ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA
[en] 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 Schrodinger 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 similar to 10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.
[Institute of Pure and Applied Mathematics, University of California Los Angeles, Los Angeles, California 90095, USA > > > ; Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany]
