基于第一性原理验证的通用机器学习势的分子动力学揭示了GaN(0001)上与生长相关的吸附物种的动态基本过程
Molecular dynamics with a first-principles-validated universal machine-learning potential reveals dynamic elementary processes of growth-related adspecies on GaN(0001)
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中文总结 AI 辅助
研究GaN MOVPE表面基本过程，结合FPMD与通用机器学习原子间势UMA，揭示吸附物种扩散模式等动态过程，单点UMA计算能重现能量，长时间MD揭示新动力学，是MLIP在GaN MOVPE分子动力学中的首次应用。
中文摘要 AI 辅助
对GaN金属有机气相外延（MOVPE）中表面基本过程的原子尺度理解，目前依赖于静态密度泛函理论（DFT）能量学和仅限于几十皮秒的第一性原理分子动力学（FPMD）。本文将FPMD与通用机器学习原子间势（MLIP）UMA相结合，以追踪GaN(0001)上与生长相关的吸附物种在纯第一性原理方法无法达到的时间尺度上的动力学。FPMD模拟揭示了一种前所未有的扩散模式，单点UMA计算能重现沿轨迹的第一性原理相对能量。基于MLIP的长时间MD（150 ps）揭示了FPMD窗口内未观察到的动力学。这项工作是MLIP在GaN MOVPE分子动力学中的首次应用。
英文摘要
Atomic-scale understanding of the surface elementary processes in metalorganic vapor phase epitaxy (MOVPE) of GaN has so far relied on static density-functional-theory (DFT) energetics and on first-principles molecular dynamics (FPMD) limited to a few tens of picoseconds. Here we combine FPMD with a universal machine-learning interatomic potential (MLIP), UMA, to follow the dynamics of growth-related adspecies on GaN(0001) over time scales inaccessible to purely first-principles approaches. FPMD simulations of a GaNH admolecule coexisting with H adatoms reveal a hitherto unrecognized diffusion mode, in which the N atom lifts the Ga atom of the GaNH unit off the surface layer during migration, and show that the lifted Ga abstracts an H adatom from the surface, events invisible to static DFT. Single-point UMA calculations on FPMD snapshots reproduce the first-principles relative energies along the trajectory (RMSE of about 8.5 meV/atom) without any retraining. Long-time MLIP-based MD (150 ps) then reveals dynamics never observed within the FPMD window: site-to-site H-adatom hopping, which gates the migration paths of the growth unit, and reversible dissociation of the GaNH unit into independently migrating Ga and NH adspecies. This work constitutes, to our knowledge, the first application of an MLIP to the molecular dynamics of GaN MOVPE.
