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In this article, the authors present an open-source library in JAX for deep learning on spherical surfaces, which can be used to address the challenges of sampling and robustness to rotation. The library, called Scaling Spherical CNNs, is demonstrated to match or surpass state-of-the-art performance on weather forecasting and molecular property prediction benchmarks.