Demystifying the Chemical Ordering of Multimetallic Nanoparticles

Abstract

Conspectus Multimetallic nanoparticles (NPs) have highly tunable properties due to the synergy between the different metals and the wide variety of NP structural parameters such as size, shape, composition, and chemical ordering. The major problem with studying multimetallic NPs is that as the number of different metals increases, the number of possible chemical orderings (placements of different metals) for a NP of fixed size explodes. Thus, it becomes infeasible to explore NP energetic differences with highly accurate computational methods, such as density functional theory (DFT), which has a high computational cost and is typically applied to up to a couple of hundred metal atoms. Here, we demonstrate a methodology advancing NP simulations by effectively exploring the vast materials space of multimetallic NPs and accurately identifying the ones with the most thermodynamically preferred chemical orderings. With accuracies reaching that of DFT, our methodology is applicable to practically any NP size, shape, and metal composition. We achieve this by significantly advancing the bond-centric (BC) model, a physics-based model that has been previously shown to rapidly predict bimetallic NP cohesive energies (CEs). Specifically, the BC model is trained in a way to understand how the bimetallic bond strength changes under different coordination environments present on a NP and how the metal composition of every site affects the detailed coordination environment using fractional coordination numbers. This newly modified BC model leads to an improvement from 0.331 (original model) to 0.089 eV/atom in CE predictions when compared to DFT values on a robust data set of 90 different NPs consisting of PtPd, AuPt, and AuPd NPs with varying compositions and chemical orderings. By incorporating the modified BC model into an in-house-developed genetic algorithm (GA) we can effectively and accurately predict the most stable chemical orderings of large, realistic bimetallic NPs consisting of thousands of metal atoms. This is demonstrated on AuPd bimetallic NPs, a challenging system due to the similarity in the cohesion of the two metals. By training our BC model using a unique DFT calculation on a bimetallic NP (one calculation for two metals combining together), we expand to explore the chemical ordering of multimetallic NPs. We first demonstrate the application of our methodology on a AuPdPt NP and validate our stability predictions with literature data. Then, we effectively explore the vast materials space of multimetallic NPs consisting of combinations of Au, Pt, and Pd as a function of metal composition. Our thermodynamic stability trends are presented in a ternary diagram revealing detailed, and yet, unexpected chemical ordering trends. Our computational framework can aid both experimental and computational researchers toward effectively screening multimetallic NP stability. Moreover, we provide an outlook of how this framework can be applied to catalyst discovery, high-entropy alloys, and single-atom alloys.

A First-Principles Approach to Modeling Surface Site Stabilities on Multimetallic Catalysts

“…Hence, MMAs may offer high activity and stability for a variety of reactions. − However, the large compositional phase space is impractical to examine despite significant advances in developing design principles for these systems. Considerable advances have been made in predicting the thermodynamics of metallic systems applying methods based on physical insight as well as purely data-driven approaches. ,− These studies signify the immense challenges associated with the combinatorial complexity encountered once one extends the material’s space beyond simple monometallic or dilute bimetallic systems. The number of possible configurations grows exponentially with changing the size, composition, and chemical environment.…”

Stability and Phase Transformations in Au–Pd Nanoparticles Studied by Means of Combined Monte Carlo and Molecular Dynamics Simulations

“…23 For example, the atomic distribution was studied in Au−Pd icosahedral NPs with different compositions by a modified bond-centered model. 24,25 Atomic ordering and surface segregation have been studied in Au−Pd NPs with icosahedral and cuboctahedral structures with the interatomic energy coming from a semiempirical tight binding model in the second moment approximation of the density of states. 26,27 The approaches described above are relatively easy to implement and give reasonable results.…”

One-pot synthesis of PdAuAg nanocrystals for efficient electrocatalytic oxidation of ethanol: achieving morphology control by independently adjusting metal-atom concentrations

“…Multimetallic nano-alloys, such as PdAuAg, [1][2][3][4][5] have garnered significant research interest due to the synergistic effects between their constituent metals and their promising applications in electrocatalysis and biomedical engineering. [6][7][8][9][10][11][12][13][14] For instance, Chen and colleagues synthesized a series of ternary PdAuAg nanoalloys supported on carbon nanotubes, which exhibited remarkable activity and unexpectedly high stability for the formate oxidation reaction in an alkaline environment. 4 Du and coworkers reported the synthesis of Pd-Au-Ag nanocages that functioned as high-performance electrocatalysts for the oxidation of both ethylene glycol and glycerol, with mass activities significantly surpassing those of commercial Pd/C catalysts.…”