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Ga2O3 is a wide-band-gap semiconductor of great interest for applications inelectronics and optoelectronics. Two-dimensional (2D) Ga2O3 synthesized fromtop-down or bottom-up processes can reveal brand new heterogeneous structuresand promising applications. In this paper, we study phase transitions amongthree low-energy stable Ga2O3 monolayer configurations using density functionaltheory and a newly developed machine-learning Gaussian approximation potential,together with solid-state nudged elastic band calculations. Kinetic minimumenergy paths involving direct atomic jump as well as concerted layer motion areinvestigated. The low phase transition barriers indicate feasible tunability ofthe phase transition and orientation via strain engineering and externalelectric fields. Large-scale calculations using the newly trainedmachine-learning potential on the thermally activated single-atom jumps revealthe clear nucleation and growth processes of different domains. The resultsprovide useful insights to future experimental synthesis and characterizationof 2D Ga2O3 monolayers.

Phase Transition of Two-Dimensional Ferroelectric and Paraelectric Ga2O3 Monolayer: A Density Functional Theory and Machine-Learning Study