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A deep neural network interatomic potential for studying thermal conductivity of β-Ga2O3

Ruiyang Li, Zeyu Liu, Andrew Rohskopf, Kiarash Gordiz, Asegun Henry, Eungkyu Lee, Tengfei Luo


β-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 β-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 β-Ga2O3 with this potential, and the calculated thermal conductivity values agree well with experimental results from 200 to 500 K. 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 β-Ga2O3 and serves as a good example of exploring thermal transport physics of complex semiconductor materials.
The authors would like to thank ONR MURI (No. N00014-18-1-2429) for the financial support. The simulations are supported by the Notre Dame Center for Research Computing and NSF through the eXtreme Science and Engineering Discovery Environment (XSEDE) computing resources provided by Texas Advanced Computing Center (TACC) Stampede II under Grant No. TG-CTS100078.
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