Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data
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+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The resulting model enables billion-scale screening, evaluating 1.1 billion atomistic structures in 50 seconds, compressing a workload that would require years o...
| Authors | Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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