Transformers Discover Molecular Structure Without Graph Priors
This work develops a systematic understanding of how physical patterns can alternatively be discovered directly from data by training a model without domain-specific priors, including any manually defined atomistic pairwise interactions, and finds that the model autonomously recovers key physical structure.
T. Kreiman, Yu-Tong Bai, Fadi Atieh et al.
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