Screening of a point charge: a fixed-node diffusion Monte Carlo study

TL;DR

This study uses fixed-node diffusion Monte Carlo to analyze static screening in a Hubbard-like model, revealing the surprising accuracy of RPA near the Mott transition and discussing implications for superconductivity in doped Fullerenes.

Contribution

It introduces an efficient method for optimizing Gutzwiller parameters in Monte Carlo calculations involving complex trial functions.

Findings

RPA is accurate near the Mott transition

Efficient Gutzwiller parameter optimization method

Implications for superconductivity in doped Fullerenes

Abstract

We study the static screening in a Hubbard-like model using fixed-node diffusion Monte Carlo. We find that the random phase approximation is surprisingly accurate even for metallic systems close to the Mott transition. As a specific application we discuss the implications of the efficient screening for the superconductivity in the doped Fullerenes. In the Monte Carlo calculations we use trial functions with two Gutzwiller-type parameters. To deal with such trial functions, we introduce a method for efficiently optimizing the Gutzwiller parameters, both in variational and in fixed-node diffusion Monte Carlo.