Author

Jun-jie Wang

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#machine learning Jan 2026

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

The results demonstrate that large-scale unlabeled spectra mined from the literature can serve as a practical and effective data source for training NMR shift models, suggesting a broader role of literature-derived, weakly structured data in data-centric AI for science.

Yongqi Jin, Ye Wang, Jun-jie Wang et al. · arXiv.org · 1 citation

#machine learning Mar 2026

SpecXMaster Technical Report

This work proposes SpecXMaster, an intelligent framework leveraging Agentic Reinforcement Learning (RL) for NMR molecular spectral interpretation that enables automated extraction of multiplicity information from both 1H and 13C spectra directly from raw FID (free induction decay) data.

Yu-Tang Ge, Ya-Ning Cui, Han-Zheng Li et al. · arXiv.org · 0 citations