Uploaded June 2024 | Updated September 2026, 1 week ago
This video was produced when the laboratory operated as the National Renewable Energy Laboratory (NREL). The laboratory is now the National Laboratory of the Rockies (NLR). Artificial intelligence (AI) has the potential to play a crucial role in accelerated catalyst design, discovery, and optimization of chemical processes for decarbonization. With the help of a range of AI tools—such as machine learning, deep learning, and large language models—researchers can uncover useful guidelines for designing new and improved catalysts, for both low- and high- technology readiness level research activities.
In this webinar, Argonne National Laboratory researcher Rajeev Assary described how AI and high-fidelity, first-principles simulations can help identify cost-efficient catalysts for deoxygenation chemistry. He also described recent efforts on using machine learning to field billions of molecules to choose the best as liquid organic hydrogen carriers. Finally, he briefly covered ongoing research directions for helping the catalysis community incorporate large language models in catalyst discovery.
This video was produced when the laboratory operated as the National Renewable Energy Laboratory (NREL). The laboratory is now the National Laboratory of the Rockies (NLR). Artificial intelligence (AI) has the potential to play a crucial role in accelerated catalyst design, discovery, and optimization of chemical processes for decarbonization. With the help of a range of AI tools—such as machine learning, deep learning, and large language models—researchers can uncover useful guidelines for designing new and improved catalysts, for both low- and high- technology readiness level research activities.
In this webinar, Argonne National Laboratory researcher Rajeev Assary described how AI and high-fidelity, first-principles simulations can help identify cost-efficient catalysts for deoxygenation chemistry. He also described recent efforts on using machine learning to field billions of molecules to choose the best as liquid organic hydrogen carriers. Finally, he briefly covered ongoing research directions for helping the catalysis community incorporate large language models in catalyst discovery.
