ML-Accelerated DFT Sampling of Catalytic Processes at Heterogeneous Interfaces - Al Fortunelli

Explore machine learning-accelerated density functional theory (DFT) sampling techniques for studying catalytic processes at heterogeneous interfaces in this 29-minute conference talk by Al Fortunelli from CNR. Delivered as part of the Interfaces and Mixing in Fluids, Plasmas, and Materials conference at the Kavli Institute for Theoretical Physics, delve into the intersection of quantum metrology, condensed matter physics, and catalysis research. Gain insights into how advanced computational methods are enhancing our understanding of fundamental physics and enabling new applications in fields ranging from dark matter searches to gravitational wave detection. Discover the potential for cross-disciplinary collaboration and innovation in quantum technologies, particle physics, and materials science.

ML-accelerated DFT sampling of catalytic processes at heterogeneous interfaces ▸ Al Fortunelli (CNR)