Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties

Christensen, Anders S. and von Lilienfeld, O. Anatole. (2019) Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties. Chimia, 73 (12). pp. 1028-1031.

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Official URL: https://edoc.unibas.ch/94205/

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The identification and use of structure-property relationships lies at the heart of the chemical sciences. Quantum mechanics forms the basis for the unbiased virtual exploration of chemical compound space (CCS), imposing substantial compute needs if chemical accuracy is to be reached. In order to accelerate predictions of quantum properties without compromising accuracy, our lab has been developing quantum machine learning (QML) based models which can be applied throughout CCS. Here, we briefly explain, review, and discuss the recently introduced operator formalism which substantially improves the data efficiency for QML models of common response properties.
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:Swiss Chemical Society
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:01 Sep 2023 10:19
Deposited On:03 Apr 2023 09:08

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