The Royal Swedish Academy of Sciences has awarded the 2025 Nobel Prize in chemistry to Susumu Kitagawa, Richard Robson and Omar M Yaghi for their work in the development of metal organic frameworks (MOF). The three scientists, who won the award on Wednesday, come from the universities of Kyoto in Japan, Melbourne in Australia and Berkeley in the United States, respectively. Such constructions can be used to harvest water from desert air, capture carbon dioxide, store toxic gases or break down traces of pharmaceuticals in the environment. "Metal organic frameworks have enormous potential, bringing previously unforeseen opportunities for custom-made materials with new functions," said Heiner Linke, chair of the Nobel Committee for Chemistry. According to Olof Ramstrom, a member of the Nobel Committee for Chemistry, the new form of molecular architecture can be compared with the handbag of the fictional Harry Potter character Hermione Granger: small on the outside but very large on the inside.

Over the past decade, climate change has become an increasing problem with one of the major contributing factors being carbon dioxide (CO2) emissions; almost 51% of total US carbon emissions are from factories. Current materials used in CO2 capture are lacking either in efficiency, sustainability, or cost. Electrocatalysis of CO2 is a new approach where CO2 can be reduced and the components used industrially as fuel, saving transportation costs, creating financial incentives. Metal Organic Frameworks (MOFs) are crystals made of organo-metals that adsorb, filter, and electrocatalyze CO2. The current available MOFs for capture & electrocatalysis are expensive to manufacture and inefficient at capture. The goal therefore is to computationally design a MOF that can adsorb CO2 and catalyze carbon monoxide & oxygen with low cost. A novel active transfer learning neural network was developed, utilizing transfer learning due to limited available data on 15 MOFs. Using the Cambridge Structural Database with 10,000 MOFs, the model used incremental mutations to fit a trained fitness hyper-heuristic function. Eventually, a Selenium MOF (C18MgO25Se11Sn20Zn5) was converged on. Through analysis of predictions & literature, the converged MOF was shown to be more effective & more synthetically accessible than existing MOFs, showing the model had an understanding of effective electrocatalytic structures in the material space. This novel network can be implemented for other gas separations and catalysis applications that have limited training accessible datasets.