A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910978368602112 |
|---|---|
| author | Cheng, Mouyang Fu, Chu-Liang Yu, Bowen Rha, Eunbi Chotrattanapituk, Abhijatmedhi Abernathy, Douglas L Cheng, Yongqiang Li, Mingda |
| author_facet | Cheng, Mouyang Fu, Chu-Liang Yu, Bowen Rha, Eunbi Chotrattanapituk, Abhijatmedhi Abernathy, Douglas L Cheng, Yongqiang Li, Mingda |
| contents | Defects are ubiquitous in solids and strongly influence materials' mechanical and functional properties. However, non-destructive characterization and quantification of defects, especially when multiple types coexist, remain a long-standing challenge. Here we introduce DefectNet, a foundation machine learning model that predicts the chemical identity and concentration of substitutional point defects with multiple coexisting elements directly from vibrational spectra, specifically phonon density-of-states (PDoS). Trained on over 16,000 simulated spectra from 2,000 semiconductors, DefectNet employs a tailored attention mechanism to identify up to six distinct defect elements at concentrations ranging from 0.2% to 25%. The model generalizes well to unseen crystals across 56 elements and can be fine-tuned on experimental data. Validation using inelastic scattering measurements of SiGe alloys and MgB$_2$ superconductor demonstrates its accuracy and transferability. Our work establishes vibrational spectroscopy as a viable, non-destructive probe for point defect quantification in bulk materials, and highlights the promise of foundation models in data-driven defect engineering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00725 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra Cheng, Mouyang Fu, Chu-Liang Yu, Bowen Rha, Eunbi Chotrattanapituk, Abhijatmedhi Abernathy, Douglas L Cheng, Yongqiang Li, Mingda Materials Science Machine Learning Defects are ubiquitous in solids and strongly influence materials' mechanical and functional properties. However, non-destructive characterization and quantification of defects, especially when multiple types coexist, remain a long-standing challenge. Here we introduce DefectNet, a foundation machine learning model that predicts the chemical identity and concentration of substitutional point defects with multiple coexisting elements directly from vibrational spectra, specifically phonon density-of-states (PDoS). Trained on over 16,000 simulated spectra from 2,000 semiconductors, DefectNet employs a tailored attention mechanism to identify up to six distinct defect elements at concentrations ranging from 0.2% to 25%. The model generalizes well to unseen crystals across 56 elements and can be fine-tuned on experimental data. Validation using inelastic scattering measurements of SiGe alloys and MgB$_2$ superconductor demonstrates its accuracy and transferability. Our work establishes vibrational spectroscopy as a viable, non-destructive probe for point defect quantification in bulk materials, and highlights the promise of foundation models in data-driven defect engineering. |
| title | A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2506.00725 |