Recommendation System to Predict the d‐band Center of Core‐Shell Bimetallic Nanoparticles Catalysts

Abstract Core‐shell nanoparticles are an important class of catalytic materials due to the presence of unsaturated bonds, phase‐separated metals, and synergistic effect of more than one elements. Exploring the electronic properties such as the ‐band center () and the related catalytic properties of these materials are quite challenging due to resource consuming experiments and computationally expensive modeling of the nanoparticles. Therefore, a density functional theory (DFT) coupled recommendation system based approach is developed to predict the of the core‐shell nanoparticles. Here, the recommendation system involves completion of a core‐shell interaction matrix, where each matrix element represents the of a unique core‐shell pair. Matrix completion by predicting the interaction matrix elements is carried out using neural network. The neural network generated a low dimensional representations for each core and shell metals, which are learned iteratively to predict corresponding of the pair. The learned representations brilliantly captures the similarities and dissimilarities between metals present in shell or core. A very low train/test RMSE of 0.009/0.009 is obtained with elemental features based model. The same model is further employed to predict the ‐band width (), suggesting its transferability. This approach recommends the optimum range of / along with the combination of core‐shell metals having most efficient adsorption of species for any reaction. The results presented in this study can help experimentally design the core‐shell nanoparticles having a desired catalytic activity.