Data Mining and Computational Screening of Rashba-Dresselhaus Splitting and Optoelectronic Properties in Two-Dimensional Perovskite Materials
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| Format: | Preprint |
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2025
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| _version_ | 1866913844921630720 |
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| author | Stanton, Robert Nie, Wanyi Tretiak, Sergei Trivedi, Dhara J. |
| author_facet | Stanton, Robert Nie, Wanyi Tretiak, Sergei Trivedi, Dhara J. |
| contents | Recent developments highlighting the promise of two-dimensional perovskites have vastly increased the compositional search space in the perovskite family. This presents a great opportunity for the realization of highly performant devices, and practical challenges associated with the identification of candidate materials. High-fidelity computational screening offers great value in this regard. In this study, we carry out a multiscale computational workflow, generating a dataset of two-dimensional perovskites in the Dion-Jacobson and Ruddlesden-Popper phases. Our dataset comprises ten B-site cations, four halogens, and over 20 organic cations across over 2,000 materials. We compute electronic properties, thermoelectric performance, and numerous geometric characteristics. Furthermore, we introduce a framework for the high-throughput computation of Rashba-Dresselhaus splitting. Finally, we use this dataset to train machine learning models for the accurate prediction of band gaps, candidate Rashba-Dresselhaus materials, and partial charges. The work presented herein can aid future investigations of two-dimensional perovskites with targeted applications in mind. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_12121 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data Mining and Computational Screening of Rashba-Dresselhaus Splitting and Optoelectronic Properties in Two-Dimensional Perovskite Materials Stanton, Robert Nie, Wanyi Tretiak, Sergei Trivedi, Dhara J. Materials Science Chemical Physics Computational Physics Recent developments highlighting the promise of two-dimensional perovskites have vastly increased the compositional search space in the perovskite family. This presents a great opportunity for the realization of highly performant devices, and practical challenges associated with the identification of candidate materials. High-fidelity computational screening offers great value in this regard. In this study, we carry out a multiscale computational workflow, generating a dataset of two-dimensional perovskites in the Dion-Jacobson and Ruddlesden-Popper phases. Our dataset comprises ten B-site cations, four halogens, and over 20 organic cations across over 2,000 materials. We compute electronic properties, thermoelectric performance, and numerous geometric characteristics. Furthermore, we introduce a framework for the high-throughput computation of Rashba-Dresselhaus splitting. Finally, we use this dataset to train machine learning models for the accurate prediction of band gaps, candidate Rashba-Dresselhaus materials, and partial charges. The work presented herein can aid future investigations of two-dimensional perovskites with targeted applications in mind. |
| title | Data Mining and Computational Screening of Rashba-Dresselhaus Splitting and Optoelectronic Properties in Two-Dimensional Perovskite Materials |
| topic | Materials Science Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2505.12121 |