Reducing CO2 emissions urgently requires the deployment of large-scale carbon capture technologies, many of which are being developed using innovative materials like porous metal-organic frameworks (MOFs). However, MOFs are typically synthesized as small crystals or powders, making them unsuitable for adsorption systems due to the high-pressure drops caused by fine particles. To address this issue, MOFs must be shaped into larger particles using polymer binders. While studies have explored MOF materials combined with binders, no clear guidelines exist, and the design process is often based on trial and error. This project aims to develop a computational approach for designing MOF-binder composites with enhanced mechanical properties and controlled porosity. By utilizing parallel molecular simulations, we will predict the interface properties in a high-throughput manner. Machine learning techniques, drawing from both simulated and literature-mined data, will enable the development of a robust screening method. Additionally, our work will be integrated with the state-of-the-art Process-Informed design of tailor-made Sorbent Materials (PrISMa) platform, which connects material design, process optimization, techno-economics, and life-cycle assessment. This integration will allow for the evaluation and comparison of MOF formulations in terms of their greenhouse gas emissions throughout the lifecycle of the carbon capture plant, addressing one of the final steps in the deployment of carbon capture technologies. To achieve these goals, the Researcher will receive training in highthroughput atomistic simulation workflows, materials informatics, and machine learning for material discovery, along with a holistic engineering approach through PrISMa. Moreover, this project offers the Researcher the opportunity to apply her expertise to the field of composite design, bridging fundamental science with industry-oriented perspectives actively pursued at IMDEA Materials Institute.