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

Hansen, Katja and Montavon, Grégoire and Biegler, Franziska and Fazli, Siamac and Rupp, Matthias and Scheffler, Matthias and von Lilienfeld, O. Anatole and Tkatchenko, Alexandre and Müller, Klaus-Robert. (2013) Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. Journal of Chemical Theory and Computation, 9 (8). pp. 3404-3419.

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Official URL: http://edoc.unibas.ch/43358/

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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.
Faculties and Departments:05 Faculty of Science > Departement Chemie > Former Organization Units Chemistry > Physikalische Chemie (Lilienfeld)
UniBasel Contributors:von Lilienfeld, Anatole
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:American Chemical Society
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:25 Jan 2017 13:52
Deposited On:20 Jun 2016 09:46

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