I doubt they are going to run much AI on that machine. The national labs mostly run "traditional HPC" workloads such as fluid codes that simulate (magneto)hydrodynamics in one way or another.
ML (not AI) will probably end up driving a lot of supercomputer workloads because distributed ML training resembles modern HPC simulation codes.
"Exascale Deep Learning for Climate Analytics" won the 2018 Gordon bell using summit.
There should be more information at https://www.olcf.ornl.gov/leadership-science/
From your link:
"New Frontiers for Material Modeling via Machine Learning Techniques"
"Large scale deep neural network optimization for neutrino physics"
"High-Fidelity Simulations of Gas Turbine Stages for Model Development using Machine Learning"
"HPC4mfg – Reinforcement Learning-based Traffic Control to Optimize Energy Usage and Throughput"
"Advances in Machine Learning to Improve Scientific Discovery"