  Mitigating the climate crisis requires a rapid transition towards
lower-carbon energy. Catalyst materials play a crucial role in the
electrochemical reactions involved in numerous industrial processes key to this
transition, such as renewable energy storage and electrofuel synthesis. To
reduce the energy spent on such activities, we must quickly discover more
efficient catalysts to drive electrochemical reactions. Machine learning (ML)
holds the potential to efficiently model materials properties from large
amounts of data, accelerating electrocatalyst design. The Open Catalyst Project
OC20 dataset was constructed to that end. However, ML models trained on OC20
are still neither scalable nor accurate enough for practical applications. In
this paper, we propose task-specific innovations applicable to most
architectures, enhancing both computational efficiency and accuracy. This
includes improvements in (1) the graph creation step, (2) atom representations,
(3) the energy prediction head, and (4) the force prediction head. We describe
these contributions, referred to as PhAST, and evaluate them thoroughly on
multiple architectures. Overall, PhAST improves energy MAE by 4 to 42$\%$ while
dividing compute time by 3 to 8$\times$ depending on the targeted task/model.
PhAST also enables CPU training, leading to 40$\times$ speedups in highly
parallelized settings. Python package: \url{https://phast.readthedocs.io}.
