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

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.

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Katja-ML-JCTC.pdf (Any fulltext), 941KB
 
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 Creators:
Hansen, Katja1, Author           
Montavon, Grégoire2, Author
Biegler, Franziska2, Author
Fazli, Siamac2, Author
Rupp, Matthias3, Author
Scheffler, Matthias1, Author           
von Lilienfeld, O. Anatole4, Author
Tkatchenko, Alexandre1, Author           
Müller, Klaus-Robert2, 5, Author
Affiliations:
1Theory, Fritz Haber Institute, Max Planck Society, ou_634547              
2Machine Learning Group, TU, Berlin, Germany, ou_persistent22              
3Institute of Pharmaceutical Sciences, ETH, Zurich, Switzerland, ou_persistent22              
4Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, IL, ou_persistent22              
5Dept. of Brain and Cognitive Engineering, Korea University, Korea, ou_persistent22              

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 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.

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Language(s): eng - English
 Dates: 2013-03-122013-07-112013-07-112013-08-13
 Publication Status: Issued
 Pages: 16
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1021/ct400195d
 Degree: -

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Title: Journal of Chemical Theory and Computation
  Other : J. Chem. Theory Comput.
Source Genre: Journal
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Publ. Info: Washington, D.C. : American Chemical Society
Pages: - Volume / Issue: 9 (8) Sequence Number: - Start / End Page: 3404 - 3419 Identifier: Other: 1549-9618
CoNE: https://pure.mpg.de/cone/journals/resource/111088195283832