  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.
