Evolutionary algorithm based configuration interaction approach

TL;DR

This paper introduces a stochastic evolutionary algorithm for configuration interaction calculations, providing an affordable approximation to full CI with promising accuracy in Hubbard and molecular systems.

Contribution

It develops a novel evolutionary algorithm-based method for configuration interaction, incorporating cloning, mutation, and crossover inspired by genetic algorithms.

Findings

CI coefficient-based fitness function performs better

Accurate results in 1D Hubbard model

Effective in symmetric water bond breaking

Abstract

A stochastic configuration interaction method based on evolutionary algorithm is designed as an affordable approximation to full configuration interaction (FCI). The algorithm comprises of initiation, propagation and termination steps, where the propagation step is performed with cloning, mutation and cross-over, taking inspiration from genetic algorithm. We have tested its accuracy in 1D Hubbard problem and a molecular system (symmetric bond breaking of water molecule). We have tested two different fitness functions based on energy of the determinants and the CI coefficients of determinants. We find that the absolute value of CI coefficients is a more suitable fitness function when combined with a fixed selection scheme.