Diffusion Sampling of Adsorbate Configurations on Catalyst Surfaces

Reliable adsorption-energy estimation in heterogeneous catalysis requires sampling diverse low-energy adsorbate configurations on a slab, but existing data-driven methods rely on dense per-system placement labels that are computationally expensive to obtain and typically constrain the search to global rigid-body translations and rotations. We instead cast adsorbate placement as conditional Boltzmann sampling under an energy induced by a pretrained ML interatomic potential, which removes the need for dense supervision and exposes the full adsorbate coordinate space, including internal conformer degrees of freedom. We introduce AdsorbSample, an energy-trained conditional diffusion sampler whose source, controller, and differentiable restraint potentials are tailored to adsorbate–surface geometry, so that physically implausible configurations are suppressed in the proposal. On the OC20-Dense benchmarks, AdsorbSample achieves the strongest MLIP Pass@\(k\) across evaluated budgets and substantially improves DFT-verified success at low sampling budgets, while remaining competitive on raw structural validity and diversity.