Micron-scale heterogeneous catalysis with Bayesian force fields from first principles and active learning

Understand

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.