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Modeling electronic quantum transport with machine learning

Lopez-Bezanilla, Alejandro and von Lilienfeld, O. Anatole. (2014) Modeling electronic quantum transport with machine learning. Physical Review B, 89 (23). p. 235411.

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Official URL: http://edoc.unibas.ch/53263/

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Abstract

We present a machine learning approach to solve electronic quantum transport equations of one-dimensional nanostructures. The transmission coefficients of disordered systems were computed to provide training and test data sets to the machine. The system's representation encodes energetic as well as geometrical information to characterize similarities between disordered configurations, while the Euclidean norm is used as a measure of similarity. Errors for out-of-sample predictions systematically decrease with training set size, enabling the accurate and fast prediction of new transmission coefficients. The remarkable performance of our model to capture the complexity of interference phenomena lends further support to its viability in dealing with transport problems of undulatory nature.
Faculties and Departments:05 Faculty of Science > Departement Chemie > Chemie > Physikalische Chemie (Lilienfeld)
UniBasel Contributors:von Lilienfeld, Anatole
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:American Physical Society
ISSN:2469-9950
e-ISSN:2469-9969
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Identification Number:
Last Modified:10 May 2017 11:16
Deposited On:25 Jan 2017 14:11

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