SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
PulseAugur coverage of SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks — every cluster mentioning SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks across labs, papers, and developer communities, ranked by signal.
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New benchmark and self-supervised model advance protein fold classification
Researchers have developed TEDBench, a new large-scale benchmark for protein fold classification, designed to overcome limitations in existing datasets and models. To address performance issues with current methods, the…
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Meta-LegNet framework accelerates catalyst screening with transferable adsorption environment learning
Researchers have developed Meta-LegNet, a novel graph learning framework designed to predict surface adsorption configurations in computational catalysis. This framework utilizes SE(3)-equivariant atom-level message pas…
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New framework unifies entropic OT with neural networks on curved spaces
Researchers have introduced Entropic Riemannian Neural Optimal Transport (Entropic RNOT), a novel framework designed to handle machine learning problems involving data on curved spaces. This method unifies intrinsic ent…
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New DenSNet model enhances molecular dynamics with machine-learned electron densities
Researchers have developed DenSNet, a novel machine-learning approach for electronic structure calculations that predicts the ground-state electron density. This method utilizes SE(3)-equivariant neural networks and a $…