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.
Full text not available from this repository.
Official URL: http://edoc.unibas.ch/43358/
Downloads: Statistics Overview
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.
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 |
ISSN: | 1549-9618 |
e-ISSN: | 1549-9626 |
Note: | Publication type according to Uni Basel Research Database: Journal article |
Identification Number: |
|
Last Modified: | 25 Jan 2017 13:52 |
Deposited On: | 20 Jun 2016 09:46 |
Repository Staff Only: item control page