Underlying mechanisms of gold nanoalloys stabilization

Abstract

Gold nanoclusters have attracted significant attention due to their unique physical-chemical properties, which can be tuned by alloying with elements such as Cu, Pd, Ag, and Pt to design materials for various applications. Although Au-nanoalloys have promising applications, our atomistic understanding of the descriptors that drive their stability is far from satisfactory. To address this problem, we considered 55-atom model nanoalloys that have been synthesized by experimental techniques. Here, we combined data mining techniques for creating a large sample of representative configurations, density functional theory for performing total energy optimizations, and Spearman correlation analyses to identify the most important descriptors. Among our results, we have identified trends in core–shell formation in the AuCu and AuPd systems and an onion-like design in the AuAg system, characterized by the aggregation of gold atoms on nanocluster surfaces. These features are explained by Au’s surface energy, packing efficiency, and charge transfer mechanisms, which are enhanced by the alloys’ preference for adopting the structure of the alloying metal rather than the low-symmetry one presented by Au55. These generalizations provide insights into the interplay between electronic and structural properties in gold nanoalloys, contributing to the understanding of their stabilization mechanisms and potential applications in various fields.

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

“…is extremely large and it would be impossible to perform any type of DFT calculations for all of them. However, here we employ an interesting approach that has recently been developed [46][47][48][49][50][51][52][53] and is summarized in Fig. 1.…”

“…A more in-depth description of the protocol, together with a flow diagram of its procedures, is provided in the ESI † and in the original work. 53 The number of structures selected for the DFT calculations (x) depends on the size of the cluster, and were chosen as x = 30, 40 and 50 for the cluster sizes of n = 13, 27 and 41, respectively. Given that this procedure is repeated for each of the seven compositions selected for each size of the cluster, the number of DFT optimizations only in the screening part is 840.…”

“…The Spearman correlation coefficient r s evaluates how well one arbitrary monotonic function can describe the relationship between two variables and, therefore, is not restricted to linear correlations. 62,63 Following previous works, [50][51][52][53] we establish the correlation of several physical-chemical parameters with excess energy to search for properties that can influence alloy stabilization. All such parameters are plotted against the excess energies to show an overall picture, which can be seen in the ESI.…”

“…The number of possible configurations (NC) that can arise from these permutations,is extremely large and it would be impossible to perform any type of DFT calculations for all of them. However, here we employ an interesting approach that has recently been developed 46–53 and is summarized in Fig. 1.…”

“…A more in-depth description of the protocol, together with a flow diagram of its procedures, is provided in the ESI† and in the original work. 53…”