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-Ga2O3, 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-Ga2O3 lattice. By integrating particle swarm optimization and hierarchical clustering methods, our algorithm attains a recognition accuracy exceeding 95% for discrete point defect configurations. Furthermore, we have developed an efficient technique for randomly generating diverse intrinsic defects in large-scale β-Ga2O3 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-Ga2O3, and moreover, holds promise for understanding similar material systems, such as Al2O3, In2O3, and Sb2O3.
@article{arxiv.2401.15920,
title = {Generalized Algorithm for Recognition of Complex Point Defects in Large-Scale \beta-$\rm {Ga_2O_3}$},
author = {Mengzhi Yan and Junlei Zhao and Flyura Djurabekova and Zongwei Xu},
journal= {arXiv preprint arXiv:2401.15920},
year = {2024}
}