Achieving net-zero and ultimately carbon-negative emissions demands transformative advances in materials and chemical processes capable of capturing and utilizing carbon dioxide (CO2) at scale. A major challenge lies in designing materials and solvents that are both highly efficient and environmentally benign across the full carbon lifecycle—from separation and capture to conversion and circular reuse. In this work, we present a multiscale computational framework to accelerate the discovery and optimization of advanced materials for CO2 capture, utilization, and storage (CCUS). By integrating quantum and classical mechanics with physics-informed machine learning, we investigate structure–property–function relationships in green solvent systems such as amino acid ionic liquids (AAILs), deep eutectic solvents (DESs), and catalyst-free CO₂-reactive media. Key thermodynamic and kinetic descriptors—such as binding affinity, reaction barriers, proton transfer rates, and solvation dynamics—are quantified to guide the rational design of task-specific materials. We demonstrate how this approach enables selective and energy-efficient CO₂ transformations, offering a viable route toward distributed carbon capture and utilization. Our findings not only identify optimal solvent–solute combinations but also provide molecular insight into reaction mechanisms critical to the development of sustainable CCUS technologies. By coupling predictive modeling with sustainability metrics, this work supports the broader decarbonization roadmap advocated by the International Energy Agency (IEA), offering theoretical foundations for scalable, carbon-negative solutions in the chemical and energy sectors.
