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 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.
View on arXiv@article{cheng2025_2506.00725,
title={ A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra },
author={ Mouyang Cheng and Chu-Liang Fu and Bowen Yu and Eunbi Rha and Abhijatmedhi Chotrattanapituk and Douglas L Abernathy and Yongqiang Cheng and Mingda Li },
journal={arXiv preprint arXiv:2506.00725},
year={ 2025 }
}