Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Abstract
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 Schr\"odinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross-validation over more...
Paper Details
Title
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Published Date
Jan 31, 2012
Journal
Volume
108
Issue
5
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