A Radial Distribution\nFunction Based Recognition Algorithm\nof Point Defects in Large-Scale β‑Ga<sub>2</sub>O<sub>3</sub> Systems

The atomic configurations and concentrations of intrinsic\ndefects\nprofoundly influence the electrical and optical properties of the\nsemiconductor materials. This influence is particularly significant\nin the case of β-Ga<sub>2</sub>O<sub>3</sub>, which is a highly\npromising ultrawide bandgap semiconductor characterized by highly\ncomplex intrinsic defect configurations. Despite its importance, there\nis a notable absence of an accurate method to recognize these defects\nin large-scale atomistic computational modeling. We design an effective\nalgorithm for the explicit identification of various intrinsic point\ndefects in the β-Ga<sub>2</sub>O<sub>3</sub> lattice, which\nconstitutes the integration of the particle swarm optimization (PSO)\nand K-means clustering (K-MC) methods. Our algorithm attains the recognition\naccuracy exceeding 95%. Finally, the algorithm is applied to dynamic\nsimulations, where the feasibility of dynamic real-time detection\nis explored.