Quantum-Centric Computational Modeling for Metal Organic Frameworks in Carbon Capture

Metal-organic frameworks (MOFs) have the ability to absorb various molecules, making them promising materials for carbon capture. Due to their high versatility and tunability, computational studies are valuable in identifying potential candidates, guiding MOF design, and reducing the number of candidates that experimental material chemists need to synthesize. In this work, we adopt a quantum-centric, data-driven approach to the computational modeling of MOFs. We utilize classical features, computed using classical computers, and quantum features, derived from quantum simulations on quantum computers, to train machine learning (ML) models that predict experimental magnitudes of interest, such as carbon uptake. This work aims to blend quantum mechanics on classical computers, quantum simulations on quantum computers, and machine learning approaches to advance the research and development of new MOFs.