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Yang T, Zhou D, Ye S, Li X, Li H, Feng Y, Jiang Z, Yang L, Ye K, Shen Y, Jiang S, Feng S, Zhang G, Huang Y, Wang S, Jiang J. Catalytic Structure Design by AI Generating with Spectroscopic Descriptors. J Am Chem Soc 2023. [PMID: 38019281 DOI: 10.1021/jacs.3c09299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Generative artificial intelligence has depicted a beautiful blueprint for on-demand design in chemical research. However, the few successful chemical generations have only been able to implement a few special property values because most chemical descriptors are mathematically discrete or discontinuously adjustable. Herein, we use spectroscopic descriptors with machine learning to establish a quantitative spectral structure-property relationship for adsorbed molecules on metal monatomic catalysts. Besides catalytic properties such as adsorption energy and charge transfer, the complete spatial relative coordinates of the adsorbed molecule were successfully inverted. The spectroscopic descriptors and prediction models are generalized, allowing them to be transferred to several different systems. Due to the continuous tunability of the spectroscopic descriptors, the design of catalytic structures with continuous adsorption states generated by AI in the catalytic process has been achieved. This work paves the way for using spectroscopy to enable real-time monitoring of the catalytic process and continuous customization of catalytic performance, which will lead to profound changes in catalytic research.
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Affiliation(s)
- Tongtong Yang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
- Institute of Intelligent Innovation, Henan Academy of Sciences, Zhengzhou, Henan 451162, P. R. China
| | - Donglai Zhou
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Sheng Ye
- School of Artificial Intelligence, Anhui University, Hefei, Anhui 230601, China
| | - Xiyu Li
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Huirong Li
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Yi Feng
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Zifan Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Li Yang
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
| | - Ke Ye
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Yixi Shen
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Shuang Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Shuo Feng
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Guozhen Zhang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Yan Huang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Song Wang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jun Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
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Al-Raeei M. The bond length of the improved Rosen Morse potential, applying for: Cesium, hydrogen, hydrogen fluoride, hydrogen chloride, lithium, and nitrogen molecules. RESULTS IN CHEMISTRY 2022. [DOI: 10.1016/j.rechem.2022.100560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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