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

TL;DR

This study investigates phase transitions in 2D Ga2O3 monolayers using density functional theory and machine learning, revealing low energy barriers and domain growth mechanisms relevant for tunable electronic applications.

Contribution

It introduces a machine-learning Gaussian approximation potential for large-scale simulations of 2D Ga2O3 phase transitions, combining DFT and nudged elastic band methods.

Findings

Low phase transition barriers enable tunability via strain and electric fields.

Large-scale simulations show nucleation and growth of domains.

Insights aid experimental synthesis of 2D Ga2O3 monolayers.

Abstract

Ga2O3 is a wide-band-gap semiconductor of great interest for applications in electronics and optoelectronics. Two-dimensional (2D) Ga2O3 synthesized from top-down or bottom-up processes can reveal brand new heterogeneous structures and promising applications. In this paper, we study phase transitions among three low-energy stable Ga2O3 monolayer configurations using density functional theory and a newly developed machine-learning Gaussian approximation potential, together with solid-state nudged elastic band calculations. Kinetic minimum energy paths involving direct atomic jump as well as concerted layer motion are investigated. The low phase transition barriers indicate feasible tunability of the phase transition and orientation via strain engineering and external electric fields. Large-scale calculations using the newly trained machine-learning potential on the thermally activated single-atom jumps reveal the clear nucleation and growth processes of different domains. The results provide useful insights to future experimental synthesis and characterization of 2D Ga2O3 monolayers.