  Determining the optimal configuration of adsorbates on a slab (adslab) is
pivotal in the exploration of novel catalysts across diverse applications.
Traditionally, the quest for the lowest energy adslab configuration involves
placing the adsorbate onto the slab followed by an optimization process. Prior
methodologies have relied on heuristics, problem-specific intuitions, or
brute-force approaches to guide adsorbate placement. In this work, we propose a
novel framework for adsorbate placement using denoising diffusion. The model is
designed to predict the optimal adsorbate site and orientation corresponding to
the lowest energy configuration. Further, we have an end-to-end evaluation
framework where diffusion-predicted adslab configuration is optimized with a
pretrained machine learning force field and finally evaluated with Density
Functional Theory (DFT). Our findings demonstrate an acceleration of up to 5x
or 3.5x improvement in accuracy compared to the previous best approach. Given
the novelty of this framework and application, we provide insights into the
impact of pre-training, model architectures, and conduct extensive experiments
to underscore the significance of this approach.
