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Hansen K, Montavon G, Biegler F, et al. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. J Chem Theory Comput. 2013;9(8):3404-19doi: 10.1021/ct400195d.
Hansen, K., Montavon, G., Biegler, F., Fazli, S., Rupp, M., Scheffler, M., von Lilienfeld, O. A., Tkatchenko, A., & Müller, K. R. (2013). Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. Journal of chemical theory and computation, 9(8), 3404-19. https://doi.org/10.1021/ct400195d
Hansen, Katja, et al. "Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies." Journal of chemical theory and computation vol. 9,8 (2013): 3404-19. doi: https://doi.org/10.1021/ct400195d
Hansen K, Montavon G, Biegler F, Fazli S, Rupp M, Scheffler M, von Lilienfeld OA, Tkatchenko A, Müller KR. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. J Chem Theory Comput. 2013 Aug 13;9(8):3404-19. doi: 10.1021/ct400195d. Epub 2013 Jul 30. PMID: 26584096.
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J Chem Theory Comput. 2013 Aug 13;9(8):3404-19. doi: 10.1021/ct400195d. Epub 2013 Jul 30.

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

Journal of chemical theory and computation

Katja Hansen, Grégoire Montavon, Franziska Biegler, Siamac Fazli, Matthias Rupp, Matthias Scheffler, O Anatole von Lilienfeld, Alexandre Tkatchenko, Klaus-Robert Müller

Affiliations

  1. Fritz-Haber-Institut der Max-Planck-Gesellschaft , Berlin, Germany.
  2. Machine Learning Group , TU Berlin, Germany.
  3. Institute of Pharmaceutical Sciences, ETH Zurich , Switzerland.
  4. Argonne Leadership Computing Facility, Argonne National Laboratory , Lemont, Illinois, United States.
  5. Department of Brain and Cognitive Engineering, Korea University , Korea.

PMID: 26584096 DOI: 10.1021/ct400195d

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.

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