A deep neural network interatomic potential for studying thermal conductivity of beta-Ga2O3
RY Li and ZY Liu and A Rohskopf and K Gordiz and A Henry and E Lee and TF Luo, APPLIED PHYSICS LETTERS, 117, 152102 (2020).
beta-Ga2O3 is a wide-bandgap semiconductor of significant technological importance for electronics, but its low thermal conductivity is an impeding factor for its applications. In this work, an interatomic potential is developed for beta-Ga2O3 based on a deep neural network model to predict the thermal conductivity and phonon transport properties. Our potential is trained by the ab initio energy surface and atomic forces, which reproduces phonon dispersion in good agreement with first-principles calculations. We are able to use molecular dynamics (MD) simulations to predict the anisotropic thermal conductivity of beta-Ga2O3 with this potential, and the calculated thermal conductivity values agree well with experimental results from 200 to 500K. Green-Kubo modal analysis is performed to quantify the contributions of different phonon modes to the thermal transport, showing that optical phonon modes play a critical role in the thermal transport. This work provides a high-fidelity machine learning-based potential for MD simulation of beta-Ga2O3 and serves as a good example of exploring thermal transport physics of complex semiconductor materials.
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