An O(L^3) algorithm computes contracted Clebsch-Gordan tensor products for equivariant ML potentials using a structured angular grid and spherical Poisson bracket to handle parity-odd terms at fixed CP rank.
Geometric and physical quantities improve E(3) equivariant message passing.arXiv preprint arXiv:2110.02905
6 Pith papers cite this work, alongside 11 external citations. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
Bayesian E(3)-equivariant MLPs with joint energy-force NLL loss achieve competitive accuracy while enabling uncertainty-guided active learning, OOD detection, and calibration.
Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.
Equivariant GNNs outperform prior models on optical spectra and static permittivity prediction using RPA datasets for materials screening.
Secondary-structure-aware GNN using energy-filtered hydrogen-bond edges improves protein representation learning on standard benchmarks.
GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.
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Fast contracted Clebsch--Gordan tensor products for equivariant graph neural networks
An O(L^3) algorithm computes contracted Clebsch-Gordan tensor products for equivariant ML potentials using a structured angular grid and spherical Poisson bracket to handle parity-odd terms at fixed CP rank.
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Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
Bayesian E(3)-equivariant MLPs with joint energy-force NLL loss achieve competitive accuracy while enabling uncertainty-guided active learning, OOD detection, and calibration.
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Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.
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Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening
Equivariant GNNs outperform prior models on optical spectra and static permittivity prediction using RPA datasets for materials screening.
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Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs
Secondary-structure-aware GNN using energy-filtered hydrogen-bond edges improves protein representation learning on standard benchmarks.
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Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.