Fast and accurate modeling of molecular atomization energies with machine learning

Rupp, Matthias and Tkatchenko, Alexandre and Müller, Klaus-Robert and von Lilienfeld, O. Anatole. (2012) Fast and accurate modeling of molecular atomization energies with machine learning. Physical Review Letters, 108 (5). 058301.

PDF - Published Version

Official URL: http://edoc.unibas.ch/43360/

Downloads: Statistics Overview


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.
Faculties and Departments:05 Faculty of Science > Departement Chemie > Chemie > Physikalische Chemie (Lilienfeld)
UniBasel Contributors:von Lilienfeld, Anatole
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:American Physical Society
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
edoc DOI:
Last Modified:12 Apr 2017 12:09
Deposited On:20 Jun 2016 09:52

Repository Staff Only: item control page