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Computational catalysis is playing an increasingly significant role in thedesign of catalysts across a wide range of applications. A common task for manycomputational methods is the need to accurately compute the adsorption energyfor an adsorbate and a catalyst surface of interest. Traditionally, theidentification of low energy adsorbate-surface configurations relies onheuristic methods and researcher intuition. As the desire to performhigh-throughput screening increases, it becomes challenging to use heuristicsand intuition alone. In this paper, we demonstrate machine learning potentialscan be leveraged to identify low energy adsorbate-surface configurations moreaccurately and efficiently. Our algorithm provides a spectrum of trade-offsbetween accuracy and efficiency, with one balanced option finding the lowestenergy configuration 87.36% of the time, while achieving a 2000x speedup incomputation. To standardize benchmarking, we introduce the Open Catalyst Densedataset containing nearly 1,000 diverse surfaces and 100,000 uniqueconfigurations.

AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials