Toward improved property prediction of 2D materials using many-body quantum Monte Carlo methods

TL;DR

This paper reviews how many-body quantum Monte Carlo methods, especially Diffusion Monte Carlo, improve the accuracy of property predictions in 2D materials beyond traditional density functional theory.

Contribution

It highlights recent successful applications of DMC to 2D materials, demonstrating its advantages over standard methods for complex electron correlation effects.

Findings

DMC provides more accurate magnetic and electronic properties.

DMC captures interlayer interactions better than DFT.

DMC has been applied to a variety of 2D materials.

Abstract

The field of two-dimensional (2D) materials has grown dramatically in the last two decades. 2D materials can be utilized for a variety of next-generation optoelectronic, spintronic, clean energy, and quantum computation applications. These 2D structures, which are often exfoliated from layered van der Waals (vdW) materials, possess highly inhomogeneous electron densities and can possess short- and long-range electron correlations. The complexities of 2D materials make them challenging to study with standard mean-field electronic structure methods such as density functional theory (DFT), which relies on approximations for the unknown exchange-correlation functional. In order to overcome the limitations of DFT, highly accurate many-body electronic structure approaches such as Diffusion Monte Carlo (DMC) can be utilized. In the past decade, DMC has been used to calculate accurate magnetic, electronic, excitonic, and topological properties in addition to accurately capturing interlayer interactions and cohesion and adsorption energetics of 2D materials. This approach has been applied to 2D systems of wide interest including graphene, phosphorene, MoS$_2$, CrI$_3$, VSe$_2$, GaSe, GeSe, borophene, and several others. In this review article, we highlight some successful recent applications of DMC to 2D systems for improved property predictions beyond standard DFT.