Data-driven stabilization of NimPdn–m nanoalloys: a study using density functional theory and data mining approaches

Abstract

Green hydrogen, generated through the electrolysis of water, is a viable alternative to fossil fuels, although its adoption is hindered by the high costs associated with the catalysts. Among a wide variety of potential materials, binary nickel-palladium (NiPd) systems have garnered significant attention, particularly at the nanoscale, for their efficacious roles in catalyzing hydrogen and oxygen evolution reactions. However, our atom-level understanding of the descriptors that drive their energetic stability at the nanoscale remains largely incomplete. Here, we investigate by density functional theory calculations the descriptors that drives the stability of the Ni_mPd_n–m clusters for different sizes (n = 13, 27, 41) and compositions. To achieve our goals, a large number of trial configurations were generated and selected using data mining algorithms (k-means, t-SNE) and genetic algorithms, while the most important physical–chemical descriptors were identified using Spearman correlation analysis. We have found that core–shell formation, with the smaller Ni atoms lying in the center of the particle, plays a major role in the stabilization of the nanoalloys, and this effect causes the alloys to assume a icosahedral-fragment configuration (as the unary nickel cluster) instead of a fcc fragment (as the unary palladium cluster). However, the core–shell formation in this alloy is unique in that Pd poor compositions exhibit scattered Pd atoms on the surface. As the palladium content increases, this gives rise to the complete Pd shell. This stabilization mechanism is quantitatively supported by the different correlations observed in the number of Ni–Ni and Pd–Pd bonds with energy, in which the latter tends to decrease alloy stability. Furthermore, a notable trend is the correlation between the coordination number of Ni atoms with alloy stabilization, while the coordination of Pd atoms shows an inverse correlation.