From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

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Introduces a robust supervised pre-training approach using data from multiple chemical domains, demonstrating state-of-the-art results on key datasets and offering insights into the influence of pre-training strategies on fine-tuning.