Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: a Case Study with HydraGNN
The Journal of Supercomputing（2025）
Key words
Machine learning,Atomistic materials modeling,Distributed data parallelism,Graph foundation models,Graph neural networks,Large-scale data processing for machine learning
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