A method that distributes per-dataset decoding heads across GPUs enables pre-training of a multi-task graph neural network on 24 million atomistic structures from five datasets.
npj Computational Materials10, 154 (2024)
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Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data
A method that distributes per-dataset decoding heads across GPUs enables pre-training of a multi-task graph neural network on 24 million atomistic structures from five datasets.