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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.

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Using Kohn-Sham (KS) density functional theory, the adsorption of Ar on graphite has been computed with various conventional exchange-correlation functionals. While the local density approximation yields a reasonable estimate of equilibrium distance and energy, three generalized gradient approximated functionals fail. Extending the KS Hamiltonian by an empirical nonlocal and atom-centered potential enables quantitative predictions. The adsorption on the on-top, hollow, and bridge sites has been investigated, and it is found that the London dispersion corrected calculations prefer the hollow site which is in agreement with other studies. Furthermore, the adsorption effect of several submonolayer coverages and of the graphitic bulk has been studied.