Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning

TL;DR

This paper introduces a machine learning-enhanced quantum-embedding framework that significantly reduces computational costs for simulating strongly-correlated materials, enabling broader and more efficient quantum-mechanical studies.

Contribution

It combines quantum-embedding methods with machine learning to bypass the most expensive steps, making accurate simulations more feasible and scalable.

Findings

Accurately describes correlation effects in actinide systems

Reduces computational cost by orders of magnitude

Enables application to complex materials with arbitrary structures

Abstract

A cardinal obstacle to performing quantum-mechanical simulations of strongly-correlated matter is that, with the theoretical tools presently available, sufficiently-accurate computations are often too expensive to be ever feasible. Here we design a computational framework combining quantum-embedding (QE) methods with machine learning. This allows us to bypass altogether the most computationally-expensive components of QE algorithms, making their overall cost comparable to bare Density Functional Theory (DFT). We perform benchmark calculations of a series of actinide systems, where our method describes accurately the correlation effects, reducing by orders of magnitude the computational cost. We argue that, by producing a larger-scale set of training data, it will be possible to apply our method to systems with arbitrary stoichiometries and crystal structures, paving the way to virtually infinite applications in condensed matter physics, chemistry and materials science.