From molecules to materials: Pre-training large generalizable models for atomic property prediction
Published in International Conference on Learning Representations, 2024
Citation: Nima Shoghi, Adeesh Kolluru, John Kitchin, Zachary Ulissi, C Zitnick, Brandon Wood, International Conference on Learning Representations, 2024. https://arxiv.org/abs/2310.16802
Introduces a multi-domain pre-training strategy for molecular property prediction that learns simultaneously from diverse chemical datasets, demonstrating substantial improvements over previous methods and advancing the ability to accurately predict properties across molecules and materials.