TY - JOUR
T1 - Fast and accurate modeling of molecular atomization energies with machine learning
AU - Rupp, Matthias
AU - Tkatchenko, Alexandre
AU - Müller, Klaus Robert
AU - Von Lilienfeld, O. Anatole
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2012/1/31
Y1 - 2012/1/31
N2 - 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 ∼10kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.
AB - 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 ∼10kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.
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U2 - 10.1103/PhysRevLett.108.058301
DO - 10.1103/PhysRevLett.108.058301
M3 - Article
C2 - 22400967
AN - SCOPUS:84856512353
VL - 108
JO - Physical Review Letters
JF - Physical Review Letters
SN - 0031-9007
IS - 5
M1 - 058301
ER -