Catalytic tempering: A method for sampling rough energy landscapes by Monte Carlo

Abstract

A new Monte Carlo algorithm is presented for the efficient sampling of the Boltzmann distribution of configurations of systems with rough energy landscapes. The method is based on the introduction of a fictitious coordinate y so that the dimensionality of the system is increased by one. This augmented system has a potential surface and a temperature that is made to depend on the new coordinate y in such a way that for a small strip of the y space, called the 'normal region,' the temperature is set equal to the temperature desired and the potential is the original rough energy potential. To enhance barrier crossing outside the 'normal region,' the energy barriers are reduced by truncation (with preservation of the potential minima) and the temperature is made to increase with |y|. The method, called catalytic tempering or CAT, is found to greatly improve the rate of convergence of Monte Carlo sampling in model systems and to eliminate the quasi-ergodic behavior often found in the sampling of rough energy landscapes.