A Radial Distribution Function Based Recognition Algorithm of Point Defects in Large-Scale β-Ga2O3 Systems

Abstract

The atomic configurations and concentrations of intrinsic defects profoundly influence the electrical and optical properties of the semiconductor materials. This influence is particularly significant in the case of β-Ga₂O₃, which is a highly promising ultrawide bandgap semiconductor characterized by highly complex intrinsic defect configurations. Despite its importance, there is a notable absence of an accurate method to recognize these defects in large-scale atomistic computational modeling. We design an effective algorithm for the explicit identification of various intrinsic point defects in the β-Ga₂O₃ lattice, which constitutes the integration of the particle swarm optimization (PSO) and K-means clustering (K-MC) methods. Our algorithm attains the recognition accuracy exceeding 95%. Finally, the algorithm is applied to dynamic simulations, where the feasibility of dynamic real-time detection is explored.