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This paper introduces Equivariant Sheaf Neural Networks (ESNN), which learn directed geometric transport on graphs to enhance equivariant message passing for physical systems while preserving exact Euclidean symmetry.
This paper introduces a framework for E3-equivariant uncertainty quantification in tensor-valued geometric learning, ensuring symmetric positive-definite covariances via matrix exponentiation and proposing a robust Log-Euclidean Equivariant Scoring Objective.
The paper introduces EquiFiLM, a lightweight extension that adds continuous external conditioning to equivariant foundation machine learning force fields via Feature-wise Linear Modulation, achieving significant accuracy improvements with minimal training data.
This paper introduces SpinGTP, a method using spin-weighted spherical harmonics to achieve complete and scalable E(3)-equivariant networks for 3D atomistic simulations, recovering antisymmetric interactions lost in prior Gaunt Tensor Product approaches.
MALOQ introduces a massively accelerated machine learning model for predicting density functional theory Hamiltonian/density matrices, enabling electronic-structure calculations for systems with up to 100k atoms using an SO(2)-equivariant backbone and scalable graph distribution, achieving over 30% time-per-epoch reduction on the Alps supercomputer.