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Accelerating SO(2)-Equivariant Materials Models through Low-Precision Emulation
Rotationally equivariant architectures improve the physical fidelity of machine-learned (ML) universal materials models based on graph neural networks (GNNs). However, their scalability is limited by the use of non-trivial, high-precision tensor operations during both training and inference. Adoptin
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