Complex ${\mathrm{Ga}}_2{\mathrm{O}}_3$ Polymorphs Explored by Accurate and General-Purpose Machine-Learning Interatomic Potentials

Abstract

${\mathrm{Ga}}_2{\mathrm{O}}_3$ is a wide-bandgap semiconductor of emergent importance for applications in electronics and optoelectronics. However, vital information of the properties of complex coexisting ${\mathrm{Ga}}_2{\mathrm{O}}_3$ polymorphs and low-symmetry disordered structures is missing. In this work, we develop two types of kernel-based machine-learning Gaussian approximation potentials (ML-GAPs) for ${\mathrm{Ga}}_2{\mathrm{O}}_3$ with high accuracy for $\beta$/$\kappa$/$\alpha$/$\delta$/$\gamma$ polymorphs and generality for disordered stoichiometric structures. We release two versions of interatomic potentials in parallel, namely soapGAP and tabGAP, for excellent accuracy and exceeding speedup, respectively. We systematically show that both the soapGAP and tabGAP can reproduce the structural properties of all the five polymorphs in an exceptional agreement with ab initio results, meanwhile boost the computational efficiency with $5\times 10^2$ and $2\times 10^5$ computing speed increases compared to density functional theory, respectively. The results show that the liquid-solid phase transition proceeds in three different stages, a "slow transition", "fast transition" and "only Ga migration". We show that this complex dynamics can be understood in terms of different behavior of O and Ga sublattices in the interfacial layer.

arXiv comment13 pages, 7 figures

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• Dissimilar thermal transport properties in $\kappa$-Ga${}_2$O${}_3$ and $\beta$-Ga${}_2$O${}_3$ revealed by machine-learning homogeneous nonequilibrium molecular dynamics simulations
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• Exploring the energy landscape of aluminas through machine learning interatomic potential
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• Improved capabilities of the TurboGAP code for radiation induced cascade simulations: an illustration with silicon
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