用生成模型实现钼硫薄膜多模态数据互推，加速材料表征。
Cross-Modal Characterization of Thin Film MoS$_2$ Using Generative Models
- 通过生成模型实现显微与光谱数据相互转换。
- 成功从拉曼光谱重建原子力显微图像特征。
- 为材料快速高效表征提供新范式，适合材料研发者。
材料生长与表征通常依赖大量专家经验与资源。常规方法需结合多种表征手段评估样品质量。机器学习可通过历史数据提升材料生长与表征的效率。本研究探索利用拉曼光谱数据预测原子力显微图像中的定量指标的可行性。训练生成模型以互相生成拉曼、光致发光谱及钼硫薄膜的原子力显微图像特征。结果表明该方法具有潜力，为材料跨模态表征提供了基础框架，可实现更快速、高效、低成本的材料发现。
原文摘要 · Abstract (English)
The growth and characterization of materials using empirical optimization typically requires a significant amount of expert time, experience, and resources. Several complementary characterization methods are routinely performed to determine the quality and properties of a grown sample. Machine learning (ML) can support the conventional approaches by using historical data to guide and provide speed and efficiency to the growth and characterization of materials. Specifically, ML can provide quantitative information from characterization data that is typically obtained from a different modality. In this study, we have investigated the feasibility of projecting the quantitative metric from microscopy measurements, such as atomic force microscopy (AFM), using data obtained from spectroscopy measurements, like Raman spectroscopy. Generative models were also trained to generate the full and specific features of the Raman and photoluminescence spectra from each other and the AFM images of the thin film MoS$_2$. The results are promising and have provided a foundational guide for the use of ML for the cross-modal characterization of materials for their accelerated, efficient, and cost-effective discovery.
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