Accelerated prediction of dielectric functions in solar cell materials with graph neural networks
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914085564579840 |
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| author | Ginter, Caden Choudhary, Kamal Mandal, Subhasish |
| author_facet | Ginter, Caden Choudhary, Kamal Mandal, Subhasish |
| contents | We present an atomistic line graph neural network (ALIGNN) model for predicting dielectric functions directly from crystal structures. Trained on $\sim$7000 dielectric functions from the JARVIS-DFT database computed with a meta-GGA exchange-correlation functional, the model accurately reproduces spectral features, including peak intensities and overall line shapes, while enabling efficient high-throughput screening. Applied to the recently developed Alexandria materials database, containing over four hundred thousand insulating materials, we uncover a clear elemental trend, with vanadium emerging as a strong indicator of materials with high-spectroscopic limited maximum efficiency (SLME). In particular, vanadium-based perovskite materials show a substantially higher fraction of high-SLME compounds compared to the database average, underscoring their promise for optoelectronic applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08738 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerated prediction of dielectric functions in solar cell materials with graph neural networks Ginter, Caden Choudhary, Kamal Mandal, Subhasish Materials Science Other Condensed Matter We present an atomistic line graph neural network (ALIGNN) model for predicting dielectric functions directly from crystal structures. Trained on $\sim$7000 dielectric functions from the JARVIS-DFT database computed with a meta-GGA exchange-correlation functional, the model accurately reproduces spectral features, including peak intensities and overall line shapes, while enabling efficient high-throughput screening. Applied to the recently developed Alexandria materials database, containing over four hundred thousand insulating materials, we uncover a clear elemental trend, with vanadium emerging as a strong indicator of materials with high-spectroscopic limited maximum efficiency (SLME). In particular, vanadium-based perovskite materials show a substantially higher fraction of high-SLME compounds compared to the database average, underscoring their promise for optoelectronic applications. |
| title | Accelerated prediction of dielectric functions in solar cell materials with graph neural networks |
| topic | Materials Science Other Condensed Matter |
| url | https://arxiv.org/abs/2510.08738 |