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

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.