Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Rupp, Matthias, Tkatchenko, Alexandre, Müller, Klaus-Robert, von Lilienfeld, O. Anatole

arXiv.org Machine Learning 

Cross-validation on 7165 molecules yields a mean absolute error of 9.9 kcal/mol, which is an order of magnitude more accurate than counting bonds or semiempirical quantum chemistry. We use the GDB data base, a library of nearly one billion organic molecules that are stable and synthetically accessible according to organic chemistry rules [15]. While potentially applicable to any stoichiometry, as a proof of principle we restrict ourselves to small organic molecules. Specifically, we define a controlled test-bed consisting of all 7165 organic molecules from the GDB data base with up to seven "heavy" atoms that contain C, N, O, or S, being saturated with hydrogen atoms. Atomization energies range from -800 to -2000 kcal/mol.

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