Generalized Algorithm for Recognition of Complex Point Defects in Large-Scale β-$\rm {Ga_2O_3}$
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916117761490944 |
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| author | Yan, Mengzhi Zhao, Junlei Djurabekova, Flyura Xu, Zongwei |
| author_facet | Yan, Mengzhi Zhao, Junlei Djurabekova, Flyura Xu, Zongwei |
| contents | 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 $β$-$\rm {Ga_2O_3}$, 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 $β$-$\rm {Ga_2O_3}$ 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 $β$-$\rm {Ga_2O_3}$ 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 $β$-$\rm {Ga_2O_3}$, and moreover, holds promise for understanding similar material systems, such as $\rm {Al_2O_3}$, $\rm {In_2O_3}$, and $\rm {Sb_2O_3}$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15920 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generalized Algorithm for Recognition of Complex Point Defects in Large-Scale β-$\rm {Ga_2O_3}$ Yan, Mengzhi Zhao, Junlei Djurabekova, Flyura Xu, Zongwei Materials Science Data Analysis, Statistics and Probability 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 $β$-$\rm {Ga_2O_3}$, 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 $β$-$\rm {Ga_2O_3}$ 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 $β$-$\rm {Ga_2O_3}$ 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 $β$-$\rm {Ga_2O_3}$, and moreover, holds promise for understanding similar material systems, such as $\rm {Al_2O_3}$, $\rm {In_2O_3}$, and $\rm {Sb_2O_3}$. |
| title | Generalized Algorithm for Recognition of Complex Point Defects in Large-Scale β-$\rm {Ga_2O_3}$ |
| topic | Materials Science Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2401.15920 |