Modeling quantum physics with machine learning

ORAL

Abstract

Machine Learning (ML) is a systematic way of inferring new results from sparse information. It directly allows for the resolution of computationally expensive sets of equations by making sense of accumulated knowledge and it is therefore an attractive method for providing computationally inexpensive 'solvers' for some of the important systems of condensed matter physics. In this talk a non-linear regression statistical model is introduced to demonstrate the utility of ML methods in solving quantum physics related problem, and is applied to the calculation of electronic transport in 1D channels.

*DOE contract number DE-AC02-06CH11357

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Authors

  • Alejandro Lopez-Bezanilla

    • Argonne National Laboratory
    • Physical Sciences and Engineering, Argonne National Laboratory, Argonne, Illinois 60439, USA

  • Louis-Francois Arsenault

    • Columbia University

  • Andrew Millis

    • Columbia University
    • Department of Physics, Columbia University, New York, NY 10027, USA
    • Department of Physics, Columbia University
    • Columbia Univ

  • Peter Littlewood

    • Argonne National Laboratory
    • Physical Sciences and Engineering, Argonne National Laboratory, Argonne, Illinois 60439, USA
    • Univ of Chicago

  • O. Anatole von Lilienfeld

    • University of Basel
    • Department of Chemistry, University of Basel, Basel, Switzerland