Journal
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Volume 9, Issue 8, Pages 3404-3419Publisher
AMER CHEMICAL SOC
DOI: 10.1021/ct400195d
Keywords
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Funding
- European Research Council (ERC)
- World Class University Program through the National Research Foundation of Korea
- Ministry of Education, Science, and Technology [R31-10008]
- Einstein Foundation
- U.S. Department of Energy, Basic Energy Sciences, Office of Science [DE-AC02-06CH11357]
- Natural Sciences and Engineering Research Council of Canada
- DFG [MU 987/17-1]
- FP7 programme of the European Community [Marie Curie IEF 273039]
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The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio calculations have been proposed as an efficient approach for describing the energies of molecules in their given ground-state structure throughout chemical compound space (Rupp et al. Phys. Rev. Lett. 2012, 108, 058301). In this paper we outline a number of established machine learning techniques and investigate the influence of the molecular representation on the methods performance. The best methods achieve prediction errors of 3 kcal/mol for the atomization energies of a wide variety of molecules. Rationales for this performance improvement are given together with pitfalls and challenges when applying machine learning approaches to the prediction of quantum-mechanical observables.
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