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Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
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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.
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Metadata
- Authors
- Hansen, K., Montavon, G., Biegler, F., Fazli, S., Rupp, M., Scheffler, M., von Lilienfeld, O. A., Tkatchenko, A., & Müller, K.-R.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-07-30
- Volume
- 9
- Issue
- 8
- Pages
- 3404-3419
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W1998260904
- Cited by
- 613
- References
- 87
- FWCI
- 8.73
- Type
- journal article
- Language
- en
- Open access
- green
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