  Computational catalysis is playing an increasingly significant role in the
design of catalysts across a wide range of applications. A common task for many
computational methods is the need to accurately compute the adsorption energy
for an adsorbate and a catalyst surface of interest. Traditionally, the
identification of low energy adsorbate-surface configurations relies on
heuristic methods and researcher intuition. As the desire to perform
high-throughput screening increases, it becomes challenging to use heuristics
and intuition alone. In this paper, we demonstrate machine learning potentials
can be leveraged to identify low energy adsorbate-surface configurations more
accurately and efficiently. Our algorithm provides a spectrum of trade-offs
between accuracy and efficiency, with one balanced option finding the lowest
energy configuration 87.36% of the time, while achieving a 2000x speedup in
computation. To standardize benchmarking, we introduce the Open Catalyst Dense
dataset containing nearly 1,000 diverse surfaces and 100,000 unique
configurations.
