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用量子力学计算与表面相互作用的原子力

Quantitatively Determining Surface-Adsorbate Properties from Vibrational Spectroscopy with Interpretable Machine Learning.

机构信息

Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China.

School of Chemistry and Chemical Engineering, Qilu University of Technology (Shandong Academy of Science), Jinan 250353, China.

出版信息

J Am Chem Soc. 2022 Sep 7;144(35):16069-16076. doi: 10.1021/jacs.2c06288. Epub 2022 Aug 24.

Abstract

Learning microscopic properties of a material from its macroscopic measurables is a grand and challenging goal in physical science. Conventional wisdom is to first identify material structures exploiting characterization tools, such as spectroscopy, and then to infer properties of interest, often with assistance of theory and simulations. This indirect approach has limitations due to the accumulation of errors from retrieving structures from spectral signals and the lack of quantitative structure-property relationship. A new pathway directly from spectral signals to microscopic properties is highly desirable, as it would offer valuable guidance toward materials evaluation and design via spectroscopic measurements. Herein, we exploit machine-learned vibrational spectroscopy to establish quantitative spectrum-property relationships. Key interaction properties of substrate-adsorbate systems, including adsorption energy and charge transfer, are quantitatively determined directly from Infrared and Raman spectroscopic signals of the adsorbates. The machine-learned spectrum-property relationships are presented as mathematical formulas, which are physically interpretable and therefore transferrable to a series of metal/alloy surfaces. The demonstrated ability of quantitative determination of hard-to-measure microscopic properties using machine-learned spectroscopy will significantly broaden the applicability of conventional spectroscopic techniques for materials design and high throughput screening under operando conditions.

摘要

从宏观可测量中学习材料的微观性质是物理科学中的一个宏伟而具有挑战性的目标。传统的方法是首先利用光谱等表征工具来识别材料结构,然后根据理论和模拟来推断感兴趣的性质。这种间接的方法由于从光谱信号中提取结构时误差的积累以及缺乏定量的结构-性质关系而受到限制。因此,人们非常希望有一种从光谱信号直接到微观性质的新途径,因为它可以通过光谱测量为材料评估和设计提供有价值的指导。在这里,我们利用机器学习的振动光谱来建立定量的光谱-性质关系。通过吸附质的红外和拉曼光谱信号,直接定量确定了基底-吸附质体系的关键相互作用性质,包括吸附能和电荷转移。机器学习的光谱-性质关系以数学公式的形式呈现,这些公式具有物理可解释性,因此可以转移到一系列金属/合金表面。使用机器学习光谱学定量测定难以测量的微观性质的能力,将大大拓宽传统光谱技术在操作条件下进行材料设计和高通量筛选的适用性。

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