  The development of machine learned potentials for catalyst discovery has
predominantly been focused on very specific chemistries and material
compositions. While effective in interpolating between available materials,
these approaches struggle to generalize across chemical space. The recent
curation of large-scale catalyst datasets has offered the opportunity to build
a universal machine learning potential, spanning chemical and composition
space. If accomplished, said potential could accelerate the catalyst discovery
process across a variety of applications (CO2 reduction, NH3 production, etc.)
without additional specialized training efforts that are currently required.
The release of the Open Catalyst 2020 (OC20) has begun just that, pushing the
heterogeneous catalysis and machine learning communities towards building more
accurate and robust models. In this perspective, we discuss some of the
challenges and findings of recent developments on OC20. We examine the
performance of current models across different materials and adsorbates to
identify notably underperforming subsets. We then discuss some of the modeling
efforts surrounding energy-conservation, approaches to finding and evaluating
the local minima, and augmentation of off-equilibrium data. To complement the
community's ongoing developments, we end with an outlook to some of the
important challenges that have yet to be thoroughly explored for large-scale
catalyst discovery.
