Modeling of molecular atomization energies using machine learning
Abstract
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. Our scheme maps the problem of solving the molecular time-independent Schrödinger equation onto a non-linear statistical regression problem. Kernel ridge regression models are trained on and compared to reference atomization energies computed using density functional theory (PBE0 approximation to Kohn-Sham level of theory). 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 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:
-
- Technical Univ. of Berlin (Germany). Machine Learning Group
- Max-Planck Society, Berlin (Germany). Fritz-Haber-Inst.
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1629374
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Cheminformatics
- Additional Journal Information:
- Journal Volume: 4; Journal Issue: S1; Conference: 7.German Conference on Chemoinformatics: 25 CIC-Workshop, Goslar (Germany), 6-8 Nov 2011; Journal ID: ISSN 1758-2946
- Publisher:
- Chemistry Central Ltd.
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Density Functional Theory; Machine Learning; Organic Molecule; Gaussian Kernel; Energy Curve
Citation Formats
Rupp, Matthias, Tkatchenko, Alexandre, Müller, Klaus-Robert, and von Lilienfeld, O. Anatole. Modeling of molecular atomization energies using machine learning. United States: N. p., 2012.
Web. doi:10.1186/1758-2946-4-S1-P33.
Rupp, Matthias, Tkatchenko, Alexandre, Müller, Klaus-Robert, & von Lilienfeld, O. Anatole. Modeling of molecular atomization energies using machine learning. United States. https://doi.org/10.1186/1758-2946-4-S1-P33
Rupp, Matthias, Tkatchenko, Alexandre, Müller, Klaus-Robert, and von Lilienfeld, O. Anatole. Tue .
"Modeling of molecular atomization energies using machine learning". United States. https://doi.org/10.1186/1758-2946-4-S1-P33. https://www.osti.gov/servlets/purl/1629374.
@article{osti_1629374,
title = {Modeling of molecular atomization energies using machine learning},
author = {Rupp, Matthias and Tkatchenko, Alexandre and Müller, Klaus-Robert and von Lilienfeld, O. Anatole},
abstractNote = {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. Our scheme maps the problem of solving the molecular time-independent Schrödinger equation onto a non-linear statistical regression problem. Kernel ridge regression models are trained on and compared to reference atomization energies computed using density functional theory (PBE0 approximation to Kohn-Sham level of theory). 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 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.},
doi = {10.1186/1758-2946-4-S1-P33},
journal = {Journal of Cheminformatics},
number = S1,
volume = 4,
place = {United States},
year = {Tue May 01 00:00:00 EDT 2012},
month = {Tue May 01 00:00:00 EDT 2012}
}
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