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20222026
most citedMulti-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems

21 citations · 41 across the 7 of their papers we have counts for

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cs.AI2026★ 1 cited

Automated Data Readiness for Scientific AI

Sean R. Wilkinson, Valentine G. Anantharaj, Jong Youl Choi +8

Leadership computing facilities steward large-scale scientific datasets that routinely require substantial transformation before serving as AI training data. However, no existing f…

cond-mat.mtrl-sci2026

Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data

Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta +8

We present an exascale workflow for materials discovery using atomistic graph foundation models built on HydraGNN. We jointly train on 16 open first-principles datasets (544+ milli…