Generalized Algorithm for Recognition of Complex Point Defects in Large-Scale β-Ga_2O_3

The electrical and optical properties of semiconductor materials are profoundly influenced by the atomic configurations and concentrations of intrinsic defects. This influence is particularly significant in the case of β-Ga_2O_3, a vital 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. In this work, we present an effective algorithm designed explicitly for identifying various intrinsic point defects in the β-Ga_2O_3 lattice. By integrating particle swarm optimization and hierarchical clustering methods, our algorithm attains a recognition accuracy exceeding 95 Furthermore, we have developed an efficient technique for randomly generating diverse intrinsic defects in large-scale β-Ga_2O_3 systems. This approach facilitates the construction of an extensive atomic database, crucially instrumental in validating the recognition algorithm through a substantial number of statistical analyses. Finally, the recognition algorithm is applied to a molecular dynamics simulation, accurately describing the evolution of the point defects during high-temperature annealing. Our work provides a useful tool for investigating the complex dynamical evolution of intrinsic point defects in β-Ga_2O_3, and moreover, holds promise for understanding similar material systems, such as Al_2O_3, In_2O_3, and Sb_2O_3.