Title: Machine-Learning Prediction of CO Adsorption in Thiolated, Ag-Alloyed Au Nanoclusters

Journal Article · · Journal of the American Chemical Society

Here we propose a machine-learning model, based on the random-forest method, to predict CO adsorption in thiolate protected nanoclusters. Two phases of feature selection and training, based initially on the Au25 nanocluster, are utilized in our model. One advantage to a machine-learning approach is that correlations in defined features disentangle relationships among the various structural parameters. For example, in Au25, we find that features based on the distribution of Ag atoms relative to the CO adsorption site are the most important in predicting adsorption energies. Our machine-learning model is easily extended to other Au-based nanoclusters, and we demonstrate predictions about CO adsorption on Ag-alloyed Au36 and Au133 nanoclusters.

Research Organization:
West Virginia University, Morgantown, WV (United States)
Sponsoring Organization:
China Postdoctoral Science Foundation; Chinese Academy of Sciences; National Natural Science Foundation of China; Synfuels China, Co. Ltd; USDOE Office of Science (SC)
Grant/Contract Number:
SC0004737
OSTI ID:
1610747
Journal Information:
Journal of the American Chemical Society, Journal Name: Journal of the American Chemical Society Journal Issue: 50 Vol. 140; ISSN 0002-7863
Publisher:
American Chemical Society (ACS)Copyright Statement
Country of Publication:
United States
Language:
English

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