  Catalyst discovery and optimization is key to solving many societal and
energy challenges including solar fuels synthesis, long-term energy storage,
and renewable fertilizer production. Despite considerable effort by the
catalysis community to apply machine learning models to the computational
catalyst discovery process, it remains an open challenge to build models that
can generalize across both elemental compositions of surfaces and adsorbate
identity/configurations, perhaps because datasets have been smaller in
catalysis than related fields. To address this we developed the OC20 dataset,
consisting of 1,281,040 Density Functional Theory (DFT) relaxations
(~264,890,000 single point evaluations) across a wide swath of materials,
surfaces, and adsorbates (nitrogen, carbon, and oxygen chemistries). We
supplemented this dataset with randomly perturbed structures, short timescale
molecular dynamics, and electronic structure analyses. The dataset comprises
three central tasks indicative of day-to-day catalyst modeling and comes with
pre-defined train/validation/test splits to facilitate direct comparisons with
future model development efforts. We applied three state-of-the-art graph
neural network models (CGCNN, SchNet, Dimenet++) to each of these tasks as
baseline demonstrations for the community to build on. In almost every task, no
upper limit on model size was identified, suggesting that even larger models
are likely to improve on initial results. The dataset and baseline models are
both provided as open resources, as well as a public leader board to encourage
community contributions to solve these important tasks.
