Yang Tongtong, Zhou Donglai, Ye Sheng, Li Xiyu, Li Huirong, Feng Yi, Jiang Zifan, Yang Li, Ye Ke, Shen Yixi, Jiang Shuang, Feng Shuo, Zhang Guozhen, Huang Yan, Wang Song, Jiang Jun
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.
J Am Chem Soc. 2023 Dec 13;145(49):26817-26823. doi: 10.1021/jacs.3c09299. Epub 2023 Nov 29.
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.
生成式人工智能为化学研究中的按需设计描绘了一幅美好的蓝图。然而，少数成功的化学生成案例仅能实现少数特殊的性质值，因为大多数化学描述符在数学上是离散的或不可连续调节的。在此，我们使用光谱描述符结合机器学习，为金属单原子催化剂上的吸附分子建立了定量的光谱结构-性质关系。除了吸附能和电荷转移等催化性质外，还成功反演了吸附分子完整的空间相对坐标。光谱描述符和预测模型具有通用性，可转移到几个不同的体系中。由于光谱描述符的连续可调性，实现了人工智能在催化过程中生成具有连续吸附态催化结构的设计。这项工作为利用光谱实时监测催化过程和连续定制催化性能铺平了道路，这将给催化研究带来深刻变革。
