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Machine learning for quantum mechanics in a nutshell

International journal of quantum chemistry, 2015-08, Vol.115 (16), p.1058-1073 [Peer Reviewed Journal]

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Title:
Machine learning for quantum mechanics in a nutshell
Author: Rupp, Matthias
Subjects: implementation ; kernel ridge regression ; machine learning ; quantum chemistry ; tutorial
Is Part Of: International journal of quantum chemistry, 2015-08, Vol.115 (16), p.1058-1073
Description: Models that combine quantum mechanics (QM) with machine learning (ML) promise to deliver the accuracy of QM at the speed of ML. This hands‐on tutorial introduces the reader to QM/ML models based on kernel learning, an elegant, systematically nonlinear form of ML. Pseudocode and a reference implementation are provided, enabling the reader to reproduce results from recent publications where atomization energies of small organic molecules are predicted using kernel ridge regression. © 2015 Wiley Periodicals, Inc. Models that combine quantum mechanics (QM) with machine learning (ML) aim to deliver the accuracy of QM at the speed of ML by interpolating between a feasible number of reference calculations. This hands‐on tutorial introduces the reader to models based on kernel learning, an elegant, systematically nonlinear form of ML. Pseudocode, a reference implementation, and an example dataset are provided.
Publisher: Hoboken: Blackwell Publishing Ltd
Language: English;French;German
Identifier: ISSN: 0020-7608
EISSN: 1097-461X
DOI: 10.1002/qua.24954
CODEN: IJQCB2
Source: Alma/SFX Local Collection

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