Artificial intelligence and material design in carbon capture and utilization: A review of emerging synergies

Climate change is driven by large greenhouse gas emissions, which have raised an alarm in efforts to reduce atmospheric carbon dioxide levels with carbon capture, utilization, and storage (CCUS), drawing attention to decarbonization efforts. This review examines the convergence of next-generation materials science and digital technologies in upgrading CCUS systems, wherein advancements in adsorbents, membranes, and catalysts for carbon capture and utilization are carefully examined. The paper further explores how digitalization, through artificial intelligence, machine learning, Internet of Things (IoT), and data analytics, is transforming CCUS process monitoring, optimization, and materials discovery. The studies demonstrate interesting findings in the domain of AI-coupled material systems, which have accelerated the screening of over 260,000 potential structures, reduced heat requirements by up to 50% in temperature swing adsorption processes, and improved carbon capture efficiency by 20% while decreasing energy consumption by 15%. However, widespread CCUS implementation faces significant challenges, including scalability, high costs (USD 70–150 million for initial deployment), geographical mismatches between emission sources and storage sites, and public concerns about environmental risks.