Large Scale Computation of Nuclear Ground States with Machine Learning and Supercomputing

Solving many-body quantum systems for their ground state energy is a computationally complex challenge with a rich history. In this talk, we present the latest developments in a scalable algorithm using machine learning surrogate models for nuclear wavefunctions, which scales to leadership class supercomputers and large nuclei. The focus of this talk will be on the computational techniques and machine learning aspects of the algorithm, as well as the path towards nuclei of experimental interest (such as Germanium and up to Xenon) on exascale super computers.