Fast and Accurate Modeling of Molecular Atomization Energies with   Machine Learning
  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. 
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert M\"uller, O. Anatole   von Lilienfeld
physics.chem-ph cond-mat.dis-nn cond-mat.mtrl-sci stat.ML