Computational catalysis and machine learning communities have made considerable progress in developing machine learning models for catalyst discovery and design. Yet, a general machine learning potential that spans the chemical space of catalysis is still out of reach. A significant hurdle is obtaining access to training data across a wide range of materials. One important class of materials where data is lacking are oxides, which inhibits models from studying the Oxygen Evolution Reaction and oxide electrocatalysis more generally. To address this we developed the Open Catalyst 2022(OC22) dataset, consisting of 62,521 Density Functional Theory (DFT) relaxations ( 9,884,504 single point calculations) across a range of oxide materials, coverages, and adsorbates (*H, *O, *N, *C, *OOH, *OH, *OH2, *O2, *CO).

Testing, testing: DeepMind sits AI down for an IQ test. While the AI performance results are not staggering in trumping or matching human reasoning, it is a start. AI scientists recognize that establishing their capacity to reason about abstract concepts has proven difficult. DeepMind wanted to see how AI could perform and the team proposed a dataset and challenge to probe abstract reasoning. Can AI match our abilities for abstract reasoning?