Modeling of molecular atomization energies using machine learning
نویسندگان
چکیده
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.
منابع مشابه
Fast and accurate modeling of molecular atomization energies with machine learning.
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 densit...
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In a recent Letter [1], the authors construct a machine learning (ML) model of molecular atomization energies, which they compare to bond counting (BC) and the PM6 semiempirical method [2]. However, their ML model was trained and tested on density functional theory (DFT) energies while BC and PM6 are fit to standard enthalpies. For fair comparison, bond energies are refit to DFT data and PM6 is...
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