Physical Review Letters · 2012 · 2.3K citations · 31 references
EngineeringMachine LearningComputational ChemistryChemistryAtomization EnergiesMolecular DesignMolecular ComputingOrganic MoleculesData SciencePhysic Aware Machine LearningAccurate ModelingBiophysicsPhysicsPhysical ChemistryMolecular MechanicQuantum ChemistryNatural SciencesMolecular PropertyMolecular Atomization EnergiesCross Validation
We introduce a machine learning model to predict atomization energies of diverse organic molecules using only nuclear charges and atomic positions. The model maps the Schrödinger equation to a nonlinear regression problem, training on hybrid DFT‑computed atomization energies of diverse organic molecules using only nuclear charges and atomic positions. Cross‑validation on over 7,000 molecules shows a mean absolute error of ~10 kcal/mol, and the model can predict atomization potential energy curves.
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 density-functional theory. Cross validation over more than seven thousand organic molecules yields a mean absolute error of ∼10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.
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Self-Consistent Equations Including Exchange and Correlation Effects
W. Kohn, L. J. Sham · Physical Review · 1965 · 61.8K citations · Full text