Learning Structure-Thermal Property Relationships in 2D Materials

POSTER

Abstract

Two dimensional monolayer semiconductors, alloys and patterned lateral heterostructures are extremely promising candidates for the next generation of nanoelectronic devices. Quantification of thermal transport of such two dimensional materials and heterostructures is necessary for the design of such nanoelectronic and thermoelectric devices. However, direct experimental measurements of intrinsic thermal conductivity is challenging at these length scales and, therefore, the role of material stoichiometry and phase distribution on thermal transport properties of these materials remains unknown.
Here, we use fully atomistic non-equilibrium molecular dynamics simulations to compare the calculated intrinsic thermal conductivity of a Mo1-xWxSe2 monolayer alloy with that of a self-similar fractal MoSe2/WSe2 heterostructure. Machine learning applied to the compositional phase space of these materials is used to predict heterostructures with desired thermal transport properties.

Presenters

  • Nitish Baradwaj

    University of Southern California

Authors

  • Nitish Baradwaj

    University of Southern California

  • Aravind Krishnamoorthy

    University of Southern California, Physics & Astronomy, University of Southern California

  • Aiichiro Nakano

    University of Southern California, Physics, University of Southern California, Physics & Astronomy, University of Southern California

  • Rajiv Kalia

    University of Southern California, Physics, University of Southern California, Physics & Astronomy, University of Southern California

  • Priya Vashishta

    University of Southern California, Physics, University of Southern California, Collaboratory for Advanced Computing and Simulations, University of Southern California, Physics & Astronomy, University of Southern California