dorsal/arxiv
View SchemaA Neuroevolution Potential for Gallium Oxide: Accurate and Efficient Modeling of Polymorphism and Swift Heavy-Ion Irradiation
| Authors | Yaohui Gu, Binbo Li, Lingyang Jiang, Yuhui Hu, Wenqiang Liu, Lijun Xu, Pengfei Zhai, Jie Liu, Jinglai Duan |
|---|---|
| Categories | |
| ArXiv ID | 2601.10174vv1 |
| URL | https://arxiv.org/abs/2601.10174 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
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.
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"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.",
"arxiv_id": "2601.10174",
"authors": [
"Yaohui Gu",
"Binbo Li",
"Lingyang Jiang",
"Yuhui Hu",
"Wenqiang Liu",
"Lijun Xu",
"Pengfei Zhai",
"Jie Liu",
"Jinglai Duan"
],
"categories": [
"cond-mat.mtrl-sci"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "A Neuroevolution Potential for Gallium Oxide: Accurate and Efficient Modeling of Polymorphism and Swift Heavy-Ion Irradiation",
"url": "https://arxiv.org/abs/2601.10174",
"version": "v1"
},
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