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Nannuzzi C, Mino L, Bordiga S, Pedersen AH, Houghton JM, Vennestrøm PN, Janssens TV, Berlier G. Optimization of high surface area VOx/TiO2 catalysts for low-temperature NH3-SCR for NOx abatement. J Catal 2023. [DOI: 10.1016/j.jcat.2023.03.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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Wang X, Jiang S, Hu W, Ye S, Wang T, Wu F, Yang L, Li X, Zhang G, Chen X, Jiang J, Luo Y. Quantitatively Determining Surface-Adsorbate Properties from Vibrational Spectroscopy with Interpretable Machine Learning. J Am Chem Soc 2022; 144:16069-16076. [PMID: 36001497 DOI: 10.1021/jacs.2c06288] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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.
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Affiliation(s)
- Xijun Wang
- 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
| | - Shuang Jiang
- 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
| | - Wei Hu
- School of Chemistry and Chemical Engineering, Qilu University of Technology (Shandong Academy of Science), Jinan 250353, China
| | - Sheng Ye
- 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 Artificial Intelligence, Anhui University, Hefei 230601, China
| | - Tairan Wang
- 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
| | - Fan Wu
- 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
| | - Li Yang
- 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
| | - Xiyu Li
- 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
| | - Guozhen Zhang
- 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
| | - Xin Chen
- GuSu Laboratory of Materials, Suzhou 215123, China
| | - Jun Jiang
- 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.,Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
| | - Yi Luo
- 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.,Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
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