Modeling electronic quantum transport with machine learning

Alejandro Lopez-Bezanilla and O. Anatole von Lilienfeld
Phys. Rev. B 89, 235411 – Published 11 June 2014
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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.

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  • Received 15 October 2013
  • Revised 3 April 2014

DOI:https://doi.org/10.1103/PhysRevB.89.235411

©2014 American Physical Society

Authors & Affiliations

Alejandro Lopez-Bezanilla1,* and O. Anatole von Lilienfeld2,3,†

  • 1Materials Science Division, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, Illinois 60439, USA
  • 2Argonne Leadership Computing Facility, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, Illinois 60439, USA
  • 3Institute of Physical Chemistry, Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland

  • *alejandrolb@gmail.com
  • anatole.vonlilienfeld@unibas.ch

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Issue

Vol. 89, Iss. 23 — 15 June 2014

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