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cs.LG2025
Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data
Massimiliano Lupo Pasini, Jong Youl Choi, Pei Zhang +6
Graph foundation models using graph neural networks promise sustainable, efficient atomistic modeling. To tackle challenges of processing multi-source, multi-fidelity data during p…
cs.LG2024★ 11 cited
Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN
Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta +8
We present our work on developing and training scalable, trustworthy, and energy-efficient predictive graph foundation models (GFMs) using HydraGNN, a multi-headed graph convolutio…