A Neuroevolution Potential for Gallium Oxide: Accurate and Efficient Modeling of Polymorphism and Swift Heavy-Ion Irradiation

TL;DR

This paper introduces a new machine-learning interatomic potential for gallium oxide that improves accuracy and efficiency, enabling better simulation of phase transformations and irradiation effects.

Contribution

The authors develop a neuroevolution-based interatomic potential for Ga2O3 that outperforms existing models and systematically enhances training for specific physical phenomena.

Findings

The NEP potential achieves higher accuracy and efficiency than tabGAP.

Simulations of heavy-ion irradiation match experimental results.

The model explains phase transformation discrepancies in Ga2O3.

Abstract

Gallium oxide (Ga2O3) is a wide-bandgap semiconductor with promising applications in high-power and high-frequency electronics. However, its complex polymorphic nature poses substantial challenges for fundamental studies, particularly in understanding phase-transformation behaviors under nonequilibrium conditions. Here, we develop a robust, accurate, and computationally efficient machine-learning interatomic potential (MLIP) for Ga2O3 based on the neuroevolution potential (NEP) framework combined with an energy-dependent weighting strategy. The resulting NEP potential demonstrates clear advantages over the state-of-the-art tabGAP potential with respect to both accuracy and computational efficiency. Furthermore, we introduce a physically process-oriented sampling strategy to systematically augment the training dataset, thereby enhancing the MLIP performance for targeted physical phenomena. As a representative application, a dedicated NEP potential is constructed for swift heavy-ion (SHI) irradiation simulations of \b{eta}-Ga2O3. The simulated results are in quantitative agreement with experimental observations and provide a consistent physical explanation for the reported experimental discrepancies regarding phase transformations in the ion track of \b{eta}-Ga2O3.