A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra

TL;DR

This paper introduces DefectNet, a foundation machine learning model that accurately predicts the type and concentration of point defects in materials from vibrational spectra, enabling non-destructive defect analysis.

Contribution

The work presents a novel foundation model that generalizes defect identification across multiple materials using vibrational spectra, with high accuracy and transferability.

Findings

Accurately predicts defect types and concentrations from vibrational spectra.

Generalizes well to unseen materials and elements.

Validated on experimental inelastic scattering data.

Abstract

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.