  The development of machine learning models for electrocatalysts requires a
broad set of training data to enable their use across a wide variety of
materials. One class of materials that currently lacks sufficient training data
is oxides, which are critical for the development of OER catalysts. To address
this, we developed the OC22 dataset, consisting of 62,331 DFT relaxations
(~9,854,504 single point calculations) across a range of oxide materials,
coverages, and adsorbates. We define generalized total energy tasks that enable
property prediction beyond adsorption energies; we test baseline performance of
several graph neural networks; and we provide pre-defined dataset splits to
establish clear benchmarks for future efforts. In the most general task,
GemNet-OC sees a ~36% improvement in energy predictions when combining the
chemically dissimilar OC20 and OC22 datasets via fine-tuning. Similarly, we
achieved a ~19% improvement in total energy predictions on OC20 and a ~9%
improvement in force predictions in OC22 when using joint training. We
demonstrate the practical utility of a top performing model by capturing
literature adsorption energies and important OER scaling relationships. We
expect OC22 to provide an important benchmark for models seeking to incorporate
intricate long-range electrostatic and magnetic interactions in oxide surfaces.
Dataset and baseline models are open sourced, and a public leaderboard is
available to encourage continued community developments on the total energy
tasks and data.
