Gabor Csányi - Machine learning potentials: from polynomials to message passing networks
57:56
Boris Kozinsky - Uncertainty-aware machine learning models of many-body atomic interactions
1:27:06
Daniel Schwalbe Koda: Machine learning for interatomic potentials
44:34
Florent Krzakal, "Optimal compressed sensing with spatial coupling and message passing"
23:47
Gaussian Processes
59:24
Terry Tao, Ph.D. Small and Large Gaps Between the Primes
1:22:55
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
1:05:24
"Representing atoms clouds: the foundations of atomic-scale machine learning" Prof. Michele Ceriotti
1:21:45