Using Data Mining Algorithms in Solid State Physics

ORAL

Abstract

We processed large materials databases with data mining methods such as clustering and classification in order to answer specific questions in the field of thermoelectric materials and transparent conductors. Our goal is to extract meaningful information from band structures repositories such as AFLOWLIB. Our implementation is validated using a toy database that mimics the complexity of AFLOWLIB, which has also been solved analytically. We found that even when the analytical solution is known, proper data analysis can help to understand physical phenomena.

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Authors

  • Troy Lyons

    • Central Michigan University

  • Nicholas Mecholsky

    • VSL/Catholic University of America
    • The Catholic University of America

  • Stefano Curtarolo

    • Duke University
    • Duke University, Durham NC
    • Materials Science, Electrical Engineering, Physics, and Chemistry, Duke University, Durham, North Carolina 27708, USA
    • Duke University, MEMS Department

  • Marco Buongiorno Nardelli

    • University of North Texas
    • Univ of North Texas

  • Marco Fornari

    • Central Michigan University
    • Department of Physics and Science of Advanced Materials Program, Central Michigan University