Journal
PHYSICAL REVIEW LETTERS
Volume 108, Issue 5, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.108.058301
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Funding
- IPAM, UCLA
- Office of Science of the U.S. DOE [DE-AC02-06CH11357]
- DFG [MU 987/4-2]
- EU [PASCAL2]
- Division Of Chemistry
- Direct For Mathematical & Physical Scien [0956500] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1040196] Funding Source: National Science Foundation
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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 Schrodinger 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 similar to 10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.
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