Genetic algorithm for the pair distribution function of the electron gas

TL;DR

This paper introduces a genetic algorithm approach to optimize the pair distribution function of the electron gas, achieving results that closely match Monte Carlo simulations, especially diffusion Monte Carlo, demonstrating its effectiveness.

Contribution

It presents a novel application of genetic algorithms to optimize the quantum hypernetted chain approximation for electron gas properties, improving agreement with established simulation methods.

Findings

Excellent agreement with diffusion Monte Carlo results

Genetic algorithm effectively optimizes the system energy

Method outperforms previous approximations in accuracy

Abstract

The pair distribution function of the electron gas is calculated using a parameterized generalization of quantum hypernetted chain approximation with the parameters being obtained by optimizing the system energy with a genetic algorithm. The functions so obtained are compared with Monte Carlo simulations performed by other authors in its variational and diffusion versions showing a very good agreement especially with the diffusion Monte Carlo results.