Modeling realistic structures of trimetallic alloys nanoparticles using chemically meaningful descriptors

Abstract Alloy nanoparticles (nanoalloys) find applications in many fields including catalysis and green energy technologies. However, the computational design of nanoalloys is hindered by the uncertainty in their arrangement of constituent elements within the particle, i.e. their chemical ordering. Herein, we present a method for realistic simulations of trimetallic alloy nanocrystallites, considering both lowest energy chemical ordering and thermal disorder. This approach uses Monte Carlo simulations based on a topological lattice Hamiltonian, with parameters derived from DFT simulations of carefully designed nanoalloy structures. Using this method, we characterized chemical orderings in nanoparticles composed of 79 and 338 atoms of metals with known catalytic activity in CO 2 hydrogenation, namely, Pd-Pt-Cu, Ni-Pd-Cu, and Co-Rh-Cu. Our simulations show that the thermal disorder in these alloys affects the average binding energies of reaction intermediates to the catalyst surface by up to 1.1 eV, implying their critical effect on the alloy’s surface reactivity. We show that the developed method can be used for brute-force evaluation of entropic contributions to mixing free energies in alloy nanoparticles. The proposed method efficiently generates realistic models of trimetallic nanoalloys, enabling reliable simulations of their properties for in-depth understanding and computational design of alloy nanoparticles.