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arXiv · 2607.23461

Molecular dynamics with a first-principles-validated universal machine-learning potential reveals dynamic elementary processes of growth-related adspecies on GaN(0001)

Yoshito Takaesu·Akira Kusaba·Junko Ishii·Shigenori Matsushima·Yoshihiro Kangawa
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Abstract

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.

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Yoshito Takaesu, Akira Kusaba, Junko Ishii, Shigenori Matsushima, Yoshihiro Kangawa. 2026-07-26. Molecular dynamics with a first-principles-validated universal machine-learning potential reveals dynamic elementary processes of growth-related adspecies on GaN(0001). https://arxiv.org/abs/2607.23461

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