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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2603.27316 |
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| _version_ | 1866915897284755456 |
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| author | Chen, Huicong Li, Mingqiang Ji, Zheyuan Zou, Yu |
| author_facet | Chen, Huicong Li, Mingqiang Ji, Zheyuan Zou, Yu |
| contents | Photoplasticity, the light-induced change in plastic deformation, plays a pivotal role in the mechanical durability and manufacturing of semiconductor materials. Yet, its governing mechanisms remain incompletely understood, owing to the interplay of coupled multiphysics factors. Here, we conduct high-throughput nanoindentation measurements to compile a dataset of paired hardness values in dark and light conditions. Then, we engineer physics-informed descriptors spanning electrical, mechanical, and optical properties, and identify the ten most informative features, including bandgap, breakdown field, and refractive index, to enable an interpretable machine learning framework that yields transferable design rules for light-tunable semiconductor mechanics. By identifying and predicting photoplasticity in semiconductors, this work provides a practical pathway for extracting mechanism-linked, transferable guidelines to engineer light-responsive mechanical behavior in semiconductor materials and devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_27316 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Identification and Prediction of Photoplasticity in Semiconductors Using Feature Engineering and Machine learning Chen, Huicong Li, Mingqiang Ji, Zheyuan Zou, Yu Materials Science Photoplasticity, the light-induced change in plastic deformation, plays a pivotal role in the mechanical durability and manufacturing of semiconductor materials. Yet, its governing mechanisms remain incompletely understood, owing to the interplay of coupled multiphysics factors. Here, we conduct high-throughput nanoindentation measurements to compile a dataset of paired hardness values in dark and light conditions. Then, we engineer physics-informed descriptors spanning electrical, mechanical, and optical properties, and identify the ten most informative features, including bandgap, breakdown field, and refractive index, to enable an interpretable machine learning framework that yields transferable design rules for light-tunable semiconductor mechanics. By identifying and predicting photoplasticity in semiconductors, this work provides a practical pathway for extracting mechanism-linked, transferable guidelines to engineer light-responsive mechanical behavior in semiconductor materials and devices. |
| title | Identification and Prediction of Photoplasticity in Semiconductors Using Feature Engineering and Machine learning |
| topic | Materials Science |
| url | https://arxiv.org/abs/2603.27316 |