2024/01/29 by Mengzhi Yan, Yan, Mengzhi, Zhao, Junlei +4
Materials Science · #Data Analysis #FOS: Physical sciences #Ga2O3 and related materials #Materials Science (cond-mat.mtrl-sci) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2401.15920
openalex publication_date 2024/01/29 · openalex created_date 2024/01/31 · openalex updated_date 2026/07/28
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-\rm 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-\rm 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-\rm 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-\rm Ga2O3, and moreover, holds promise for understanding similar material systems, such as \rm Al2O3, \rm In2O3, and \rm Sb2O3.