A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra
This work addresses a long-standing problem in materials science for researchers and engineers by enabling non-destructive defect characterization, though it is incremental as it builds on existing machine learning and spectroscopy methods.
The authors tackled the challenge of non-destructive identification and quantification of multiple substitutional point defects in solids by introducing DefectNet, a foundation model that predicts defect chemical identity and concentration from vibrational spectra, achieving accurate results across 56 elements and validating with experimental data on SiGe alloys and MgB$_2$.
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.