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arXiv Machine Learning By Yongqi Jin, Yecheng Wang, Jun-jie Wang, Rong Zhu, Guolin Ke, Weinan E

From Human Labels to Literature: Semi-Supervised Learning of NMR Chemical Shifts at Scale

Read the original on arXiv Machine Learning →

The paper introduces a semi‑supervised framework that learns to predict nuclear magnetic resonance (NMR) chemical shifts from millions of literature‑extracted spectra without explicit atom‑level assignments. By treating the prediction as a permutation‑invariant set supervision problem, the authors show that optimal bipartite matching can be reduced to a sorting‑based loss, enabling stable large‑scale training. The resulting models outperform state‑of‑the‑art methods, generalize better to diverse molecules, and for the first time capture systematic solvent effects across common NMR solvents.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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