Fast evaluation of real spherical harmonics and their derivatives in Cartesian coordinates
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Spherical harmonics provide a smooth, orthogonal, and symmetry-adapted basis to expand functions on a sphere, and they are used routinely in computer graphics, signal processing and different fields of science, from geology to quantum chemistry. More recently, spherical harmonics have become a key component of rotationally equivariant models for geometric deep learning, where they are used in combination with distance-dependent functions to describe the distribution of neighbors within local spherical environments within a point cloud. We present a fast and elegant algorithm for the evaluation of the real-valued spherical harmonics. Our construction integrates many of the desirable features of existing schemes and allows to compute Cartesian derivatives in a numerically stable and computationally efficient manner. We provide an efficient C implementation of the proposed algorithm, along with easy-to-use Python bindings.
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