Data Science to Optimize Metal-Organic Frameworks for Carbon Capture Applications

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Data Science to Optimize Metal-Organic Frameworks for Carbon Capture Applications

Berend Smit

Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

E-mail: Berend.Smit@EPFL.ch,

Metal-organic frameworks (MOFs) are crystalline structures composed of metal nodes and organic linkers. By combining various metal nodes and organic linkers, chemists can create an unlimited array of materials with applications in gas separation, gas storage, sensing, and catalysis. This versatility makes MOFs an ideal subject for data science. This presentation explores how data science can assist in designing MOFs specifically for carbon capture. We demonstrate how data science techniques can provide insights into challenges that conventional theories struggle to address, such as determining the metal's oxidation state and the MOF's heat capacity. Additionally, we highlight how data science can help identify the key characteristics of top-performing materials in carbon capture processes.