🔬Why There Is No "AlphaFold for Materials" — AI for Materials Discovery with Heather Kulik

The podcast explores the application of AI and machine learning in accelerating the discovery of new materials, particularly in chemistry. Heather Kulik, a professor of chemical engineering at MIT, shares her work on using AI to predict and optimize material properties, such as making tougher plastics by uncovering unexpected chemical phenomena in polymer networks. She highlights the use of active learning to solve multidimensional challenges, like optimizing metal-organic frameworks for CO2 capture, considering factors such as stability and CO2 absorption. Kulik also addresses the limitations of current machine learning models, advocating for more diverse chemical bonding data and rigorous validation to replace conventional physics-based modeling.