arXiv Machine Learning July 22, 2026 By Daniele Angioletti, Marco Nobile, Vittorio Limongelli

GEqTrain: A Configuration-Driven Framework for Retargeting Equivariant Graph Neural Networks Across 3D Scientific Tasks

Read the original on arXiv Machine Learning →

arXiv:2607. 19083v1 Announce Type: new Abstract: Equivariant graph neural networks provide a powerful modeling language for three-dimensional scientific data, but their reuse is often limited by implementations tied to specific tasks, outputs, and training regimes.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Routing by Reaching: Composition of Pre-trained GFlowNets for Multi-Objective Generation

GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond

ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table

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