Machine-learning model to predict adsorption energies in thiolated bimetallic nanoclusters

We developed a random forest based machine learning model to predict adsorption energies in Ag-alloyed thiolated gold nanoclusters. The features of this model are based only on the geometric properties of non-relaxed and adsorabte-free nanocluster. The features have been defined so that they can be applied to any nanocluster. Using Au₂₅ system as a test case, we obtained prediction accuracies of 0.173 (RMSE) and 0.779 (R2). To show the applicability of our model to nanoclusters with different sizes and shapes, we also predicted adsorption energies in Au₃₆ and Au₁₃₃ nanoclusters. Our model can be used as a filtering tool to downselect nanoclusters with desired adsorption energies for further calculations.