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Exploring GFlowNets and AI-Driven Material Discovery for Carbon Capture
Manage episode 446296977 series 3584638
Contenu fourni par Breaking Math, Gabriel Hesch, and Autumn Phaneuf. Tout le contenu du podcast, y compris les épisodes, les graphiques et les descriptions de podcast, est téléchargé et fourni directement par Breaking Math, Gabriel Hesch, and Autumn Phaneuf ou son partenaire de plateforme de podcast. Si vous pensez que quelqu'un utilise votre œuvre protégée sans votre autorisation, vous pouvez suivre le processus décrit ici https://fr.player.fm/legal.
In this episode of Breaking Math, hosts Gabriel Hesch and Autumn Phaneuf dive into the cutting-edge world of Generative Flow Networks (GFlowNets) and their role in artificial intelligence and material science. The discussion centers on how GFlowNets are revolutionizing the discovery of new materials for carbon capture, offering a powerful alternative to traditional AI models. Learn about the mechanics of GFlowNets, their advantages, and the groundbreaking results in developing materials with enhanced CO2 absorption capabilities. The episode also explores the future potential of GFlowNets in AI-driven material discovery and beyond, emphasizing their transformative impact on carbon capture technology and sustainable innovation.
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You can find the paper “Discovery of novel reticular materials for carbon dioxide capture using GFlowNets” by Cipcigan et al in Digital Discovery Journal by the Royal Society of Chemistry.
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