Thermodynamic calculations using reverse Monte Carlo: Simultaneously tuning multiple short-range order parameters for 2D lattice adsorption problem

TL;DR

This paper introduces a fast and accurate reverse Monte Carlo method for thermodynamic calculations in 2D lattice adsorption problems, capable of efficiently handling geometric frustration and multiple short-range order parameters.

Contribution

It develops a novel RMC-based approach that is computationally more efficient than traditional Monte Carlo methods for lattice thermodynamics, especially in complex frustrated systems.

Findings

RMC method achieves accuracy comparable to Metropolis MC.

The approach efficiently handles geometric frustration in adsorption.

It quickly finds the ordered adlayer configuration in 2D systems.

Abstract

Lattice simulations are an important class of problems in crystalline solids, surface science, alloys, adsorption, absorption, separation, catalysis, to name a few. We describe a fast computational method for performing lattice thermodynamic calculations that is based on the use of the reverse Monte Carlo (RMC) technique and multiple short-range order (SRO) parameters. The approach is comparable in accuracy to the Metropolis Monte Carlo (MC) method. The equilibrium configuration is determined in 5-10 Newton-Raphson iterations by solving a system of coupled nonlinear algebraic flux equations. This makes the RMC-based method computationally more efficient than MC, given that MC typically requires sampling of millions of configurations. The technique is applied to the interacting 2D adsorption problem. Unlike grand canonical MC, RMC is found to be adept at tackling geometric frustration, as it is able to quickly and correctly provide the ordered c(2x2) adlayer configuration for Cl adsorbed on a Cu (100) surface.