Fast and accurate modeling of molecular atomization energies with machine learning

Phys Rev Lett. 2012 Feb 3;108(5):058301. doi: 10.1103/PhysRevLett.108.058301. Epub 2012 Jan 31.


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 ∼10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.