  Quantum-mechanically accurate reactive molecular dynamics (MD) at the scale
of billions of atoms has been achieved for the heterogeneous catalytic system
of H$_2$/Pt(111) using the FLARE Bayesian force field. This achievement
provides accelerated time-to-solution from first principles, with Bayesian
active learning enabling efficient and autonomous training of the machine
learning model. The resulting model is then deployed in LAMMPS on GPUs using
the Kokkos performance portability library. The Bayesian force field provides
quantitative uncertainty of predictions on every atomic environment, critical
for detecting configurations in large reactive simulations that are outside of
the training set. Scaling benchmarks were performed using real-application MD
of the H$_2$/Pt(111) heterogeneous catalysis on the Summit supercomputer, with
simulations reaching 0.5 trillion atoms on 4556 GPU nodes.
