A computer model is designing crystalline materials that could pull carbon dioxide out of the air and turn it into methanol fuel. Global warming driven by CO₂ emissions demands practical ways to both capture the gas and convert it into something useful. Current methods are often inefficient or expensive. This project tackles both problems at once by simulating how a class of porous materials called covalent organic frameworks (COFs) can trap CO₂, and then how a platinum catalyst—or a metal sulfide defect embedded in the COF—can chemically reduce the captured CO₂ into methanol. If the simulations succeed, they could reveal the optimal COF structure for maximum CO₂ uptake and identify the most efficient catalytic pathway for methanol production. That would open the door to a closed-loop system: capture industrial emissions and convert them directly into a transportable fuel, reducing net atmospheric CO₂ while producing a valuable energy carrier. The work is entirely computational—using density functional theory, molecular dynamics, and machine learning—so its immediate impact is a deeper understanding of material behaviour rather than a working reactor. But that fundamental insight is the necessary first step toward building real capture-and-conversion devices.
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Global warming is a serious worldwide threat with a significant impact on ecosystems, and CO2 emission is intimately tied to this threat. This project aims to design a novel integrated research plan for CO2 capture using Covalent Organic Frameworks (COFs) and the formation of methanol in the presence of metal catalyst and metal sulfides through a series of innovative investigations. Our objectives are as follows. Objective I: We use density functional theory to predict how we can increase the CO2 uptake capacity of COF by exploring its structure. Objective II: We test the capacity of COFs to uptake CO2 in a multivariable environment by molecular dynamics simulation followed by active learning to produce an effective search algorithm for CO2 capture. Objective III: We use density functional theory to study the mechanism of CO2 reduction to methanol by Pt and by embedding a metal sulfide defect into the COF. The research and innovation objectives of the project benefit from the strong connections between its components, which have been carefully designed for effective measurement and verification. It contributes to the field by deepening our understanding of the functionality of COFs, finding an efficient search algorithm for optimal operation condition for CO2 uptake by COFs, and exploring novel pathways for methanol production. This proposal involves the use of density functional theory, molecular dynamics simulation and active learning as a subset of machine learning to predict materials properties for the use of COFs to CO2 uptake and methanol production. This ambitious effort aims to advance beyond the current state of knowledge by seamlessly integrating various computational chemistry methods. This approach will comprehensively address the potential of COFs based on i) the previous experiences of the researcher and the host and ii) transferring the knowledge and skills between them.
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