Assessment and validation of machine learning methods for predicting molecular atomization energies

Katja Hansen, Grégoire Montavon, Franziska Biegler, Siamac Fazli, Matthias Rupp, Matthias Scheffler, O. Anatole Von Lilienfeld, Alexandre Tkatchenko, Klaus Muller

Research output: Contribution to journalArticle

259 Citations (Scopus)

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. 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.

Original languageEnglish
Pages (from-to)3404-3419
Number of pages16
JournalJournal of Chemical Theory and Computation
Volume9
Issue number8
DOIs
Publication statusPublished - 2013 Aug 13

ASJC Scopus subject areas

  • Physical and Theoretical Chemistry
  • Computer Science Applications

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  • Cite this

    Hansen, K., Montavon, G., Biegler, F., Fazli, S., Rupp, M., Scheffler, M., Von Lilienfeld, O. A., Tkatchenko, A., & Muller, K. (2013). Assessment and validation of machine learning methods for predicting molecular atomization energies. Journal of Chemical Theory and Computation, 9(8), 3404-3419. https://doi.org/10.1021/ct400195d