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Found 4 papers, 3 papers with code
Technical Report: The Graph Spectral Token -- Enhancing Graph Transformers with Spectral Information
Graph Transformers have emerged as a powerful alternative to Message-Passing Graph Neural Networks (MP-GNNs) to address limitations such as over-squashing of information exchange.
Transformers are efficient hierarchical chemical graph learners
We demonstrate SubFormer on benchmarks for predicting molecular properties from chemical structures and show that it is competitive with state-of-the-art graph transformers at a fraction of the computational cost, with training times on the order of minutes on a consumer-grade graphics card.
Beyond MD17: the reactive xxMD dataset
We show that the xxMD dataset involves diverse geometries which represent chemical reactions.
Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism
Equivariant graph neural networks are a standard approach to such problems, with one of the most successful methods employing tensor products between various tensors that transform under the spatial group.
