Mitigating the climate crisis requires a rapid transition towards lower carbon energy. Catalyst materials play a crucial role in the electrochemical reactions involved in this transition, such as electrofuel synthesis, renewable fertiliser production and energy storage. In this context, there is a need to discover more effective catalysts for these reactions. Machine learning (ML) holds the potential to efficiently model the properties of materials from large amounts of data, and thus to accelerate electrocatalyst design. The Open Catalyst Project OC20 data set was constructed to that end. However, most existing ML models trained on it are still neither scalable nor accurate enough for practical applications. Here, we propose several task-specific innovations, applicable to most architectures, which increase both computational efficiency and precision. In particular, we aim to improve (1) the graph creation step, (2) atom representations and (3) the energy prediction head. We describe and evaluate these contributions across several architectures, showing up to 5$\times$ reduction in inference time without sacrificing accuracy.