A Multi-Scale Quantum Framework for Evaluating Metal-Organic Frameworks in Carbon Capture

TL;DR

This paper introduces a hierarchical quantum embedding framework for efficient and systematic evaluation of metal-organic frameworks' ability to capture CO2, aiming to improve accuracy and scalability over traditional methods.

Contribution

It presents a multi-scale quantum framework that enhances the accuracy and efficiency of simulating MOF-CO2 interactions, integrating quantum embedding and discussing quantum hardware integration.

Findings

Hierarchical cluster model improves simulation efficiency.

Quantum embedding provides systematic accuracy tuning.

Application to MOF structures shows promising results.

Abstract

Metal Organic Frameworks (MOFs) are promising materials to help mitigate the effects of global warming by selectively absorbing $\text{CO}_{2}$ for direct capture. Accurate quantum chemistry simulations are a useful tool to help select and design optimal MOF structures, replacing costly or impractical experiments or providing chemically inspired features for data-driven approaches such as machine learning. However, applying simulations over large datasets requires efficient simulation methods such as Density Functional Theory (DFT) which, despite often being accurate, introduces uncontrolled approximations and a lack of systematic improvability. In this work we outline a hierarchical cluster model that includes a recently developed quantum embedding that provides a more systematic approach to efficiently tune accuracy. We apply this workflow to calculate the binding affinity for a small set of MOF structures and $\text{CO}_{2}$ using experimentally measured heat of adsorption as a reference. Since quantum embeddings have also been proposed as a framework to accelerate the utility of quantum hardware, we discuss some of the benefits and challenges of integrating quantum solvers into the workflow outlined in this work.