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Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45970

Hansen,  Katja
Theory, Fritz Haber Institute, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons22064

Scheffler,  Matthias
Theory, Fritz Haber Institute, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons22175

Tkatchenko,  Alexandre
Theory, Fritz Haber Institute, Max Planck Society;

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Citation

Hansen, K., Montavon, G., Biegler, F., Fazli, S., Rupp, M., Scheffler, M., et al. (2013). Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. Journal of Chemical Theory and Computation, 9(8), 3404-3419. doi:10.1021/ct400195d.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-14D2-5
Abstract
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. 108: 058301, 2012). 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.