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Modeling of molecular atomization energies using machine learning
Journal of Cheminformatics volume 4, Article number: P33 (2012)
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 [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  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.
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Rupp, M., Tkatchenko, A., Müller, K. et al. Modeling of molecular atomization energies using machine learning. J Cheminform 4, P33 (2012) doi:10.1186/1758-2946-4-S1-P33
- Density Functional Theory
- Machine Learning
- Organic Molecule
- Gaussian Kernel
- Energy Curve