arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.19932cond-mat.mtrl-sci
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用于尖端增强拉曼光谱的自动化结构发现

Automated structure discovery for Tip Enhanced Raman Spectroscopy

Aalto University(阿尔托大学);WPI Nano Life Science Institute (WPI-NanoLSI), Kanazawa University(金泽大学 WPI 纳米生命科学研究所)

Harshit Sethi, Markus Junttila, Orlando J Silveira, Adam S Foster

AI总结:

本文提出一种编码器-解码器模型,通过模拟TERS图像数据实现分子结构的自动化识别,从而减少对传统量子化学计算的依赖。

摘要:

尖端增强拉曼光谱(TERS)提供了分子的纳米级化学指纹以及高分辨率的分子形貌映射,为材料发现提供了一种强大工具。然而,TERS图像数据集难以解释,通常需要耗费时间且计算强度大的量子化学计算。为解决这一问题,我们提出了一种编码器-解码器模型,该模型在平面分子的模拟TERS图像上进行训练和评估,能够从模拟的光谱数据中准确预测分子结构。我们的方法证明了从TERS图像中自动化识别分子结构的可行性,从而绕过了传统的人工分析。这些发现为将机器学习方法扩展到实验TERS数据集奠定了基础,可能通过将纳米级光谱学与自动化计算分析相结合,加速分子发现。

英文摘要:

Tip-Enhanced Raman Spectroscopy (TERS) provides nanoscale chemical fingerprints alongside high-resolution topographic mapping of molecules, offering a powerful tool for materials discovery. However, TERS image datasets are challenging to interpret and typically demand time-consuming, computationally intensive quantum-chemistry calculations. To overcome this problem, we present an encoder-decoder model trained and evaluated on simulated TERS images of planar molecules, enabling direct prediction of molecular structures from spectral simulated data with high accuracy. Our approach demonstrates the feasibility of automating molecular structure identification from TERS images, bypassing traditional manual analysis. These findings provide a foundation for extending machine learning methods to experimental TERS datasets, potentially accelerating molecular discovery by integrating nanoscale spectroscopy with automated computational analysis.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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