Reference : Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomiz...
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Physics
http://hdl.handle.net/10993/25885
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
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Hansen, Katja [Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany]
Montavon, Gregoire [Machine Learning Group, TU Berlin, Germany]
Biegler, Franziska [Machine Learning Group, TU Berlin, Germany]
Siamac, Fazli [Machine Learning Group, TU Berlin, Germany]
Rupp, Matthias [Institute of Pharmaceutical Sciences, ETH Zurich, Switzerland]
Scheffler, Matthias [Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany]
von Lilienfeld, O. Anatole [Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, Illinois, United States]
Tkatchenko, Alexandre mailto [Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany]
Mueller, Klaus-Robert [Machine Learning Group, TU Berlin, Germany > > > ; Department of Brain and Cognitive Engineering, Korea University, Korea]
2013
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
AMER CHEMICAL SOC
9
8
3404-3419
Yes
International
1549-9618
1155 16TH ST, NW, WASHINGTON, DC 20036 USA
[en] 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.
http://hdl.handle.net/10993/25885
10.1021/ct400195d
Article

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