Nanoalloy composition-temperature phase diagram for catalyst design: Case study of Ag-Au

Abstract

By coupling a cluster expansion with density functional theory (DFT) calculations, we determine the configurational thermodynamics (site preferences and occupations) for alloyed nanoparticles (NPs) as functions of composition (c) and temperature (T), exemplified using a 55-atom AgAu truncated cuboctahedron NP. The c-T phase diagram for site occupations gives detailed design information for alloyed NP, especially the thermodynamically stable active sites for catalysis and how they change with stoichiometry and processing temperature. Generally, Ag prefers core and Au prefers shell agreeing with our universal core-shell preference assessed from DFT impurity segregation energies, but with interesting multi-shell configurations having specific active sites.2

High-Throughput Survey of Ordering Configurations in MXene Alloys Across Compositions and Temperatures

“…For a given configuration, σ, its E f with respect to its binary MXenes M1 3 C 2 and M2 3 C 2 is given as where x (σ) is the fraction of M2 atoms out of the total number of transition metal atoms. For each MXene alloy, a training set of DFT-calculated E f (σ) is utilized to construct effective cluster interactions (ECIs) via the cluster expansion (CE) method. − The construction of the ECI and MC simulations are performed using the TTK code, − which is capable of generating symmetry-sorted clusters for alloy surfaces and nanostructures, − where ECIs close to the surface differ from their counterparts in the bulk. The ECI values are plotted in Figure S1 (see also Figure S2 for illustration of clusters).…”

Robust cluster expansion of multicomponent systems using structured sparsity

“…Initially, CE was largely applied to binary alloys 2, [13][14][15][16][17][18][19][20][21][22][23][24][25][26] . Thereafter, it has been applied to more complex systems, including ternary to quinary alloys 5,6,12,[27][28][29] , semiconductors 7,30 , battery materials 31,32 , clathrates 33,34 , magnetic alloys [35][36][37] , and nanoscale alloys 3,4,11,[38][39][40][41][42] . In complex systems, the reduced symmetry increases the number of symmetrically distinct clusters, exacerbating the cluster selection problem.…”

“…1 the energies of a training set of structures, usually calculated from first principles. When appropriately truncated, the CE is an accurate model for efficiently predicting the energies [2][3][4][5][6] or associated properties [7][8][9][10][11][12] of different atomic configurations. However, selecting the appropriate set of atomic clusters as descriptors is challenging: selections based on physical intuition are not robust, while those based on statistics are not physical.…”

Effect of incident energy on the configuration of Fe–Al nanoparticles, a molecular dynamics simulation of impact deposition

“…1–3 The structures of nanoalloys need to be determined to understand their properties. Several scholars have performed experimental 4–6 and theoretical methods 1,7–14 to analyze the nanoparticle structure of nanoalloys. Various mixing possibilities, such as core–shell, sub-cluster segregation, mixed, and three shell configurations, have been found.…”

“…These configurations depend on the specific choice of two types of metals and their bulk miscibility factors. 6,9,13,15 External growth conditions, such as temperature, substrate configuration, nanoparticle size, and deposition flux, were also studied because of their significant influence on nanoalloy structure. 3,7,8,16,17 The incident energy of the impact deposition atom is another factor that influences nanoalloy configuration.…”