用振动谱无损识别材料中多种点缺陷的化学种类和浓度
A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra
- 基于振动谱（声子态密度）设计注意力机制模型，直接预测缺陷种类与浓度
- 可同时识别最多6种缺陷元素，浓度范围0.2%至25%，在56种元素晶体上表现良好
- 适用于半导体、合金等复杂材料，尤其适合缺乏实验数据的缺陷工程研究
点缺陷普遍存在于固体中，并显著影响材料的力学与功能特性。然而，对多种共存缺陷进行无损表征与定量分析仍是长期挑战。本文提出DefectNet，一种用于从振动谱（特别是声子态密度，PDoS）中直接预测取代型点缺陷化学种类与浓度的通用机器学习模型。该模型在超过16,000个来自2,000种半导体的模拟谱图上训练，采用定制化注意力机制，可识别浓度在0.2%至25%之间、最多六种共存的缺陷元素。模型在56种元素的未见晶体上具有良好泛化能力，并可通过实验数据微调。通过非弹性散射测量验证了其在SiGe合金和MgB₂超导体中的准确性与可迁移性。本工作确立了振动光谱作为块体材料点缺陷定量分析的可行无损手段，凸显了基础模型在数据驱动缺陷工程中的潜力。
原文摘要 · Abstract (English)
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.
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