Journal
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
Volume 140, Issue 50, Pages 17508-17514Publisher
AMER CHEMICAL SOC
DOI: 10.1021/jacs.8b08800
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Funding
- DOE [SC-0004737]
- National Science Foundation [DMREF CHE-1434378]
- Chinese Academy of Sciences President's International Fellowship Initiative (PIFI)
- National Talent Program of the Chinese Academy of Sciences
- National Natural Science Foundation of China [21473229, 91545121]
- Synfuels China, Co. Ltd.
- National Thousand Young Talents Program of China
- Hundred-Talent Program of Chinese Academy of Sciences
- China Postdoctoral Science Foundation [2016M590216]
- Shanxi Hundred-Talent Program
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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 Au-25 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 Au-25, 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 Au-36 and Au-133 nanoclusters.
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