Mean absolute error

Linear regression

Quantum mechanics

Statistics

Density functional theory

Astrophysics

Energy (signal processing)

Atomic physics

Set (abstract data type)

Molecule

Computer science

Statistical physics

Error bar

Physics

Nonlinear system

Regression

Mathematics

Programming language

Mean squared error

Machine learning

Physical Review Letters

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.

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning