New technology for energy storage is necessary for the large-scale adoption
of renewable energy sources like wind and solar. The ability to discover
suitable catalysts is crucial for making energy storage more cost-effective and
scalable. The Open Catalyst Project aims to apply advances in graph neural
networks (GNNs) to accelerate progress in catalyst discovery, replacing Density
Functional Theory-based (DFT) approaches that are computationally burdensome.
Current approaches involve scaling GNNs to over 1 billion parameters, pushing
the problem out of reach for a vast majority of machine learning practitioner
around the world. This study aims to evaluate the performance and insights
gained from using more lightweight approaches for this task that are more
approachable for smaller teams to encourage participation from individuals from
diverse backgrounds. By implementing robust design patterns like geometric and
symmetric message passing, we were able to train a GNN model that reached a MAE
of 0.0748 in predicting the per-atom forces of adsorbate-surface interactions,
rivaling established model architectures like SchNet and DimeNet++ while using
only a fraction of trainable parameters.

新技术对于大规模采用风能和太阳能等可再生能源是必要的。发现适合的催化剂对于使能量储存更具成本效益和可扩展性至关重要。这项研究旨在评估使用更轻量级方法在这一任务中获得的性能和洞察力，以鼓励来自不同背景的个人的参与。通过实施稳健的设计模式，如几何和对称的信息传递，我们能够训练一个 GNN 模型，在仅使用一小部分可训练参数的情况下，达到了 0.0748 的平均绝对误差，与 SchNet 和 DimeNet++ 等已建立的模型架构相媲美。