volume | PIER Journals

Abstract

CO₂ conversion is a central strategy for closing the carbon cycle and enabling sustain- able energy and chemical production through catalytic pathways. In this work, a descriptor-based, data-driven computational framework is employed to screen nanocatalysts for CO₂ conversion using density functional theory data reported in the literature. Key adsorption and electronic descriptors, including CO₂∗ and CO∗ binding energies, are analyzed to establish structure-activity relationships governing catalytic performance. Correlation and volcano-type analyses reveal that moderate adsorption strengths are essential for balancing CO₂ activation and product desorption, while excessively strong binding leads to surface poisoning and reduced activity. The results demonstrate that descriptor-guided screening can effectively rank catalyst candidates and provide rational design rules, without relying on new computationally intensive simulations. This framework offers a computationally efficient pathway for accelerating nanocatalyst discovery for CO₂ conversion.

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Dr. Osama Aziz

Dr. Muhibur Rahman