AI 中文总结
该研究提出基于晶格的 KMC 框架模拟 GaN(0001)分子束外延生长，捕捉关键微观过程，支持自适应实时势垒评估，能再现岛形成等现象，为 GaN 外延及化合物半导体非平衡生长提供了灵活的原子尺度预测模拟平台。
AI 中文摘要
我们提出了一种基于晶格的动力学蒙特卡罗（KMC）框架，用于模拟 GaN(0001)的分子束外延生长。该框架在可扩展架构中捕捉了外延生长的关键微观过程，包括温度依赖的表面扩散、通量驱动的沉积、埃利希 - 施沃贝尔（ES）台阶边缘势垒、奥斯特瓦尔德熟化和特定物种的解吸，能系统探索实验相关生长条件。除预定义活化能目录外，还支持使用机器学习原子间势进行自适应实时势垒评估。预定义势垒模拟再现了紧凑三角形岛形成，还捕捉了生长中断时的奥斯特瓦尔德熟化和 ES 势垒诱导的多层成核。在高温下，解吸驱动岛‘行走’状态，N - Ga 交换在 trailing 边缘产生弱束缚 Ga 吸附原子（AdGa），AdGa 的优先解吸导致不对称边缘后退和岛的净平移。我们的 KMC 框架为 GaN 外延的原子尺度预测模拟以及更广泛的化合物半导体非平衡生长提供了灵活平台。
英文摘要
We present a lattice-based kinetic Monte Carlo (KMC) framework for simulating GaN(0001) growth by molecular beam epitaxy. The framework captures the key microscopic processes governing epitaxial growth, including temperature-dependent surface diffusion, flux-driven deposition, Ehrlich--Schwoebel (ES) step-edge barriers, Ostwald ripening, and species-specific desorption, within a scalable architecture that enables systematic exploration of experimentally relevant growth conditions. In addition to predefined activation-energy catalogs, the framework supports adaptive on-the-fly barrier evaluation using machine-learned interatomic potentials. When previously unencountered local atomic configurations arise, activation barriers are computed via nudged elastic band, potential energy scans, or Brønsted--Evans--Polanyi methods, and cached for reuse. Predefined-barrier simulations reproduce compact triangular island formation, and further capture Ostwald ripening during growth interruptions and ES barrier-induced multilayer nucleation. At elevated temperatures, desorption drives an island ``walking'' regime, in which N--Ga exchange generates weakly bound Ga adatoms (AdGa) at trailing edges; preferential desorption of AdGa leads to asymmetric edge retreat and net island translation. Our KMC framework provides a flexible platform for predictive simulations of GaN epitaxy at the atomic scale and, more broadly, non-equilibrium growth of compound semiconductors.
Journal refApplied Surface Science, 2026
DOI：10.1016/j.apsusc.2026.167867
