Speaker: Anders Johansson
Title: Toward trillion-atom exascale MD with FLARE and Allegro
Abstract: Allegro and FLARE are two very different packages for constructing machine learning potentials that are fast, accurate, and suitable for extreme-scale molecular dynamics simulations. Allegro uses PyTorch for efficient equivariant potentials with state-of-the-art accuracy, while FLARE is a sparse Gaussian process potential with an optimized C++ training backend leveraging Kokkos, OpenMP, and MPI for state-of-the-art performance, and a user-friendly Python frontend. I will give an overview of the two methods with a focus on computational efficiency and scalability and demonstrations of extreme-scale applications on the world's largest supercomputer.