Exploring Metal–Organic Framework Design Strategies for CO2Capture Using Explainable Artificial Intelligence

Abstract

Metal–organic frameworks (MOFs) have emerged as promising adsorbents for CO2 capture due to their high surface area, tunable pore size, and exceptional chemical functionality. However, the identification of optimal MOFs is hindered by the absence of efficient and interpretable high-throughput screening methods, which are capable of addressing the complexity and diversity of MOF structures. In this study, we developed predictive models for the CO2 adsorption capacity and CO2/N2 selectivity of MOFs, incorporating a wide range of pore structures, topological features, and organic linkers. These models are based on a gradient-enhanced regression framework. Through hyperparameter optimization, th..