Carbon‐Based Adsorbents for CO 2 Capture: Mechanism‐Guided Material Design and Data‐Driven Optimization

ABSTRACT Carbon capture is the upstream cornerstone of carbon capture, utilization and storage, where dispersed CO 2 is concentrated into controlled streams. As adsorbent performance and process adaptability improve, adsorption‐based capture has attracted increasing attention. Carbon‐based materials, as industrially mature adsorbents, have secured a place in this field owing to their balanced performance in cost‐effectiveness, stability, and efficiency. However, compared with crystalline porous adsorbents, their less ordered pore environments and nonpolar surface characteristics result in relatively weak affinity toward CO 2 . To address these limitations, extensive efforts have been devoted to pore engineering, surface chemistry modification, and composite construction. Nevertheless, the complex nonlinear relationships between synthesis conditions, physicochemical properties, and adsorption performance remain difficult to elucidate through empirical optimization alone. Machine learning has introduced a new paradigm to uncover key descriptors and structure–performance relationships from complex datasets, accelerating the rational design of carbon‐based materials. Despite this progress, existing studies remain fragmented in scope and focus. Therefore, this review begins with the adsorption mechanisms and evaluation criteria of carbon‐based adsorbents, with attention to the limitations of commonly reported performance metrics. Recent progress in material optimization is then discussed according to the main functional roles of different strategies, together with comparisons across representative studies. The review further examines machine learning from descriptor construction and prediction targets to model development, validation, mechanistic interpretation, and synthesis optimization. Particular attention is given to how data‐driven methods can help analyze coupled material variables, while their reliability remains limited by data quality, dataset size, and model generalization. Overall, this review summarizes current understanding of carbon adsorbent design and discusses how experimental and data‐driven approaches can be used together to support further material optimization.