DataVXThe Journal of Physical Chemistry LettersPaper
journal article·2024-04-25

Bird’s-Eye View of the Activity Distribution on a Catalyst Surface via a Machine Learning-Driven Adequate Sampling Algorithm

Hui Yang, Pengju Ren, Xiaobin Geng, Wenping Guo, James Patrick Lewis, Yong Yang, Yong-Wang Li, Xiao-Dong Wen

The Journal of Physical Chemistry Letters· Volume 15· Issue 16
Publisher page
Source abstract

Abstract

Rational design of catalysts relies on a deep understanding of the active centers. The structure and activity distribution of active centers on a surface are two of the central issues in catalysis and important targets of theoretical and experimental investigations. Herein, we report a machine learning-driven adequate sampling (MLAS) framework for obtaining a statistical understanding of the chemical environment near catalyst sites. Combined strategies were implemented to achieve highly efficient sampling, including the decomposition of degrees of freedom, stratified sampling, Gaussian process regression, and well-designed constraint optimization. The MLAS framework was applied to the rate-determining step in NH 3 synthesis, namely the N 2 activation process. We calculated the produced population function, P A, which provides a comprehensive and intuitive understanding of active centers. The MLAS framework can be broadly applied to other more complicated catalyst materials and reaction networks.
Source: openalex_inverted_index · Quality: baseline

Research context

Catalysis & surfaces

OpenAlex topics: Machine Learning in Materials Science · Electrocatalysts for Energy Conversion · Catalysis and Oxidation Reactions

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China

National Energy Center for Coal to Liquids, Synfuels China Company, Ltd., Huairou District, Beijing 101400, China

University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China

Hong Kong Quantum AI Laboratory, Ltd., Hong Kong Science Park, Hong Kong 999077, China

Funding

National Key Research and Development Program of China · Institute of Coal Chemistry, Chinese Academy of Sciences · National Science Fund for Distinguished Young Scholars · National Natural Science Foundation of China · Chinese Academy of Sciences · Beijing Advanced Innovation Center for Materials Genome Engineering, Synfuels China, Co. Ltd

References (38)

Show source-provided references
1. 10.1039/C8CP05717F10.1039/C8CP05717F ↗
2. 10.1021/acscatal.9b0517510.1021/acscatal.9b05175 ↗
3. 10.1002/anie.20161016610.1002/anie.201610166 ↗
4. 10.1038/s41467-020-19634-810.1038/s41467-020-19634-8 ↗
5. 10.1021/jacs.7b0898410.1021/jacs.7b08984 ↗
6. 10.1021/acs.jpcc.1c0174610.1021/acs.jpcc.1c01746 ↗
7. 10.1021/jp060840l10.1021/jp060840l ↗
8. 10.1021/acscatal.9b0194210.1021/acscatal.9b01942 ↗
9. 10.1021/jp904709g10.1021/jp904709g ↗
10. 10.1021/jp052668b10.1021/jp052668b ↗
11. 10.1021/acs.jpcc.9b0985610.1021/acs.jpcc.9b09856 ↗
12. 10.1016/j.jechem.2021.06.03910.1016/j.jechem.2021.06.039 ↗
13. 10.1038/s41570-021-00340-y10.1038/s41570-021-00340-y ↗
14. 10.1002/anie.20211102610.1002/anie.202111026 ↗
15. 10.1002/advs.20190161410.1002/advs.201901614 ↗
16. 10.1021/acs.accounts.1c0015310.1021/acs.accounts.1c00153 ↗
17. 10.1002/anie.20140295810.1002/anie.201402958 ↗
18. 10.1126/science.aab350110.1126/science.aab3501 ↗
19. Parr R. G. · 199510.1093/oso/9780195092769.001.0001 ↗
20. 10.1002/anie.20160182410.1002/anie.201601824 ↗
21. 10.1063/1.496467110.1063/1.4964671 ↗
22. 10.1038/s41524-023-01065-w10.1038/s41524-023-01065-w ↗
23. 10.1021/acs.jpcc.8b0538210.1021/acs.jpcc.8b05382 ↗
24. 10.1021/acscatal.2c0424610.1021/acscatal.2c04246 ↗
25. 10.1021/acscatal.2c0009010.1021/acscatal.2c00090 ↗
26. 10.1021/acsomega.2c0122210.1021/acsomega.2c01222 ↗
27. 10.1021/acscatal.9b0099410.1021/acscatal.9b00994 ↗
28. 10.1021/acs.iecr.2c0292610.1021/acs.iecr.2c02926 ↗
29. 10.1126/science.110643510.1126/science.1106435 ↗
30. 10.1021/acscatal.9b0390310.1021/acscatal.9b03903 ↗
31. 10.1021/acscatal.0c0054910.1021/acscatal.0c00549 ↗
32. 10.1103/PhysRevLett.83.181410.1103/PhysRevLett.83.1814 ↗
33. 10.1126/science.110643510.1126/science.1106435 ↗
34. 10.1021/acscatal.8b0507410.1021/acscatal.8b05074 ↗
35. 10.1021/acs.jpcc.9b0956310.1021/acs.jpcc.9b09563 ↗
36. 10.1021/jp808547810.1021/jp8085478 ↗
37. 10.1021/acscatal.8b0123210.1021/acscatal.8b01232 ↗
38. 10.1021/jacs.7b0898410.1021/jacs.7b08984 ↗