SpinGTP uses Spin-Weighted Spherical Harmonics to complete the Gaunt Tensor Product, achieving full E(3)-equivariance with GTP-like scaling and better handling of chiral and parity-odd cases.
Reducing so (3) convolutions to so (2) for efficient equivariant gnns
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Structural pruning of SO(3) equivariant atomistic models from large checkpoints yields 1.5-4x fewer parameters and 2.5-4x less pre-training compute than small models trained from scratch, while outperforming them on most Matbench Discovery metrics and downstream tasks.
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Spin-Weighted Spherical Harmonics Enable Complete and Scalable $\mathrm{E}(3)$-Equivariant Networks
SpinGTP uses Spin-Weighted Spherical Harmonics to complete the Gaunt Tensor Product, achieving full E(3)-equivariance with GTP-like scaling and better handling of chiral and parity-odd cases.
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Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning
Structural pruning of SO(3) equivariant atomistic models from large checkpoints yields 1.5-4x fewer parameters and 2.5-4x less pre-training compute than small models trained from scratch, while outperforming them on most Matbench Discovery metrics and downstream tasks.