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  • Poster presentation
  • Open Access

Modeling of molecular atomization energies using machine learning

  • 1Email author,
  • 2,
  • 1 and
  • 3
Journal of Cheminformatics20124 (Suppl 1) :P33

  • Published:


  • Density Functional Theory
  • Machine Learning
  • Organic Molecule
  • Gaussian Kernel
  • Energy Curve

Atomization energies are an important measure of chemical stability. Machine learning is used to model atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only [1]. Our scheme maps the problem of solving the molecular time-independent Schrödinger equation onto a non-linear statistical regression problem. Kernel ridge regression [2] models are trained on and compared to reference atomization energies computed using density functional theory (PBE0 [3] approximation to Kohn-Sham level of theory [4, 5]). We use a diagonalized matrix representation of molecules based on the inter-nuclear Coulomb repulsion operator in conjunction with a Gaussian kernel. Validation on a set of over 7000 small organic molecules from the GDB database [6] yields mean absolute error of ~10 kcal/mol, while reducing computational effort by several orders of magnitude. Applicability is demonstrated for prediction of binding energy curves using augmentation samples based on physical limits.

Authors’ Affiliations

Machine Learning Group, Technical University, Berlin, 10587, Germany
Fritz-Haber-Institute, Max-Planck Society, Berlin, 14195, Germany
Argonne National Laboratory, Argonne, Illinois 60439, USA


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© Rupp et al; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.