Quantum simulation of carbon capture in periodic metal-organic frameworks

• URL: http://arxiv.org/abs/2510.02550v1
• Date: Thu, 02 Oct 2025 20:37:23 GMT
• Title: Quantum simulation of carbon capture in periodic metal-organic frameworks
• Authors: Dario Rocca, Jerome F. Gonthier, Joshua Levin, Tobias Schafer, Andreas Gruneis, Byeol Kang, Hong Woo Lee,
• Abstract summary: Carbon capture is vital for decarbonizing heavy industries such as steel and chemicals.<n>This study focuses on Fe-MOF-74, a magnetic Mott insulator with exposed metal sites that enhance CO2 adsorbing.<n>We conduct quantum experiments using the sample-based quantum diagonalization method.
• Score: 0.2931680194227129
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Carbon capture is vital for decarbonizing heavy industries such as steel and chemicals. Metal-organic frameworks (MOFs), with their high surface area and structural tunability, are promising materials for CO2 capture. This study focuses on Fe-MOF-74, a magnetic Mott insulator with exposed metal sites that enhance CO2 adsorption. Its strongly correlated electronic structure challenges standard DFT methods, which often yield inconsistent predictions. We initially benchmark adsorption energies using various DFT functionals, revealing substantial variability and underscoring the need for more accurate approaches, such as those provided by quantum computing. However, practical quantum algorithms are far less established for simulations of periodic materials, particularly when the plane-wave basis set, often comprising tens of thousands of basis vectors, is used. To address this, we employ an active space reduction strategy based on Wannier functions and natural orbital selection. Localized orbitals around the adsorption site are identified, and MP2 natural orbitals are used to improve convergence of correlation energies. Adsorption energies are then computed using a quantum number-preserving ansatz within the variational quantum eigensolver framework. In addition to classical simulations, we conduct quantum experiments using the sample-based quantum diagonalization method. Although current hardware limits the size of feasible simulations, our approach offers a more efficient and scalable path forward. These results advance the applicability of quantum algorithms to realistic models of carbon capture and periodic materials more broadly.

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