Abstract

We present a lattice-based kinetic Monte Carlo (KMC) framework for simulating GaN(0001) growth by molecular beam epitaxy. The framework captures temperature-dependent surface diffusion, flux-driven deposition, Ehrlich–Schwoebel step-edge barriers, Ostwald ripening, and species-specific desorption within a scalable architecture. 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.

Abstract

We present a lattice-based kinetic Monte Carlo (KMC) framework for simulating GaN(0001) growth by molecular beam epitaxy. The framework captures temperature-dependent surface diffusion, flux-driven deposition, Ehrlich–Schwoebel step-edge barriers, Ostwald ripening, and species-specific desorption within a scalable architecture. 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.

Abstract

We present a lattice-based kinetic Monte Carlo (KMC) framework for simulating GaN(0001) growth by molecular beam epitaxy. The framework captures temperature-dependent surface diffusion, flux-driven deposition, Ehrlich–Schwoebel step-edge barriers, Ostwald ripening, and species-specific desorption within a scalable architecture. 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.

Abstract

We present a lattice-based kinetic Monte Carlo (KMC) framework for simulating GaN(0001) growth by molecular beam epitaxy. The framework captures temperature-dependent surface diffusion, flux-driven deposition, Ehrlich–Schwoebel step-edge barriers, Ostwald ripening, and species-specific desorption within a scalable architecture. 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.