İlknur Eruçar from the Faculty of Engineering is leading Work Package 4 (WP4) for the EU4MOFs COST Action  (CA22147), an initiative focused on advancing Metal-Organic Frameworks (MOFs) to address pressing societal needs in health, water, and sustainable energy. MOFs, known for their high porosity and versatile chemical properties, hold potential for transformative applications in cancer nanomedicine, wastewater treatment, and energy storage. However, realizing their full potential requires overcoming challenges in controlling MOF structures and properties across molecular, nano-, meso-, and macro-scales.

EU4MOFs aims to improve control and customization of MOF materials by refining synthesis techniques and leveraging computational screening and machine learning to optimize material properties. By bringing together experts from fields such as (bio)chemistry, materials engineering, nanomedicine, and computational science, the initiative will support the scale-up of MOF innovations from the lab to industry, contributing to societal impact. 

Eruçar organized a training school entitled “MOF Computational Tools, Machine Learning, and Databases” as part of the COST Action (CA22147). The training school aimed to provide participants with advanced knowledge and hands-on experience using data-driven approaches, machine learning techniques, and computational tools to advance research and innovation in Metal-Organic Frameworks (MOFs). The school focused on state-of-the-art simulation methods and their applications, equipping attendees with the skills necessary to address complex challenges in MOF research, from material discovery to storage solutions.