Information-theory-based solution of the inverse problem in classical statistical mechanics

TL;DR

This paper introduces an information-theory-based inverse Monte Carlo method to accurately determine interaction potentials from radial distribution functions in classical statistical mechanics, demonstrating effectiveness in high-density fluids.

Contribution

The paper presents a novel inverse Monte Carlo approach utilizing the Maximum Entropy Principle to extract interaction potentials from pair distribution data.

Findings

Accurate potential extraction for high-density monoatomic fluids

Method achieves good results with modest computational effort

Potential emerges as the asymptotic transition probability expression

Abstract

We present a procedure for the determination of the interaction potential from the knowledge of the radial pair distribution function. The method, realized inside an inverse Monte Carlo simulation scheme, is based on the application of the Maximum Entropy Principle of information theory and the interaction potential emerges as the asymptotic expression of the transition probability. Results obtained for high density monoatomic fluids are very satisfactory and provide an accurate extraction of the potential, despite a modest computational effort.