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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.21830 |
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| _version_ | 1866911339309432832 |
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| author | Zeb, M. H. Kabir, M. Z. |
| author_facet | Zeb, M. H. Kabir, M. Z. |
| contents | We tackle the challenge of predicting vibrational stability in inorganic semiconductors for high-throughput screening, an essential attribute for evaluating synthesizability alongside thermodynamic stability, frequently missing in prominent materials databases. We create a physics-informed neural network (PINN) that incorporates the Born stability requirements directly into its loss function. This integration is a key learning constraint since it only allows the model to make predictions that do not violate fundamental physics. The model shows consistent and improved performance, having been trained on a dataset of 2112 inorganic materials with validated phonon spectra, and getting an F1-score of 0.83 for both stable and unstable classes. The model shows an AUC-ROC of 0.82 on a benchmark dataset of 1296 materials. Our PINN surpasses the best models in comparative tests, especially when it comes to accurately identifying unstable materials, which is crucial for a stability filter. This work offers a comprehensive screening tool for identifying materials and a methodology for incorporating domain knowledge to enhance predictive accuracy in materials informatics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21830 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-informed Neural Network (PINN) to Predict Vibrational Stability of Inorganic Semiconductors Zeb, M. H. Kabir, M. Z. Materials Science We tackle the challenge of predicting vibrational stability in inorganic semiconductors for high-throughput screening, an essential attribute for evaluating synthesizability alongside thermodynamic stability, frequently missing in prominent materials databases. We create a physics-informed neural network (PINN) that incorporates the Born stability requirements directly into its loss function. This integration is a key learning constraint since it only allows the model to make predictions that do not violate fundamental physics. The model shows consistent and improved performance, having been trained on a dataset of 2112 inorganic materials with validated phonon spectra, and getting an F1-score of 0.83 for both stable and unstable classes. The model shows an AUC-ROC of 0.82 on a benchmark dataset of 1296 materials. Our PINN surpasses the best models in comparative tests, especially when it comes to accurately identifying unstable materials, which is crucial for a stability filter. This work offers a comprehensive screening tool for identifying materials and a methodology for incorporating domain knowledge to enhance predictive accuracy in materials informatics. |
| title | Physics-informed Neural Network (PINN) to Predict Vibrational Stability of Inorganic Semiconductors |
| topic | Materials Science |
| url | https://arxiv.org/abs/2512.21830 |