Capturing segregation behavior in metal alloy nanoparticles accurately using computer simulations is contingent upon the availability of high-fidelity interatomic potentials. The embedded atom method (EAM) potential is a widely trusted interatomic potential form used with pure metals and their alloys. When limited experimental data is available, the A-B EAM cross-interaction potential for metal alloys AxB1−x are often constructed from pure metal A and B potentials by employing a pre-defined ‘mixing rule’ without any adjustable parameters. While this approach is convenient, we show that for AuPt, NiPt, AgAu, AgPd, AuNi, NiPd, PtPd and AuPd such mixing rules may not even yield the correct alloy properties, e.g., heats of mixing, that are closely related to the segregation behavior. A general theoretical formulation based on scaling invariance arguments is introduced that addresses this issue by tuning the mixing rule to better describe alloy properties. Starting with an existing pure metal EAM potential that is used extensively in literature, we find that the mixing rule fitted to heats of mixing for metal solutions usually provides good estimates of segregation energies, lattice parameters and cohesive energy, as well as equilibrium distribution of metals within a nanoparticle using Monte Carlo simulations. While the tunable mixing rule generally performs better than non-adjustable mixing rules, the use of the tunable mixing rule may still require some caution. For e.g., in Pt–Ni system we find that the segregation behavior can deviate from the experimentally observed one at Ni-rich compositions. Despite this the overall results suggest that the same approach may be useful for developing improved cross-potentials with other existing pure metal EAM potentials as well. As a further test of our approach, mixing rule estimated from binary data is used to calculate heat of mixing in AuPdPt, AuNiPd, AuPtNi, AgAuPd and NiPtPd. Excellent agreement with experiments is observed for AuPdPt.

The Monte Carlo (MC) technique is an important tool for studying equilibrium properties of materials. When the starting configuration provided as an input to a MC calculation is far from equilibrium, an inordinate amount of computational effort may be required to bring the system closer to equilibrium in the pre-equilibration step of the MC calculation. In order to alleviate this cost, a new computational strategy is presented with the aim of rapidly generating starting off-lattice atomic structures that are already close to equilibrium. The method involves preparing a collection of on-lattice configurations using fast reverse MC calculations. Each configuration corresponds to a different value of short-range order parameter(s). Next, by performing short MC calculations with each starting structure, one measures the extent to which the distribution of local atomic arrangements has changed. The optimal configuration exhibits the smallest change in the distribution. While the optimal configuration can serve as an input to longer MC calculations, in many situations, the resulting structure may be directly used for the estimation of thermodynamic properties. Application of our approach to several off-lattice binary and ternary metal alloy systems with phase separation, good mixing, ordering, and surface segregation is demonstrated. A speed-up of >100–1000 times over the standard MC approach is achieved even with small systems containing a few thousand particles, and close-to-equilibrium structures containing million atoms can be rapidly prepared using our method within a day on a standard desktop computer.

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