SpecXMaster Technical Report
Intelligent spectroscopy serves as a pivotal element in AI-driven closed-loop scientific discovery, functioning as the critical bridge between matter structure and artificial intelligence. However, conventional expert-dependent spectral interpretation encounters substantial hurdles, including susceptibility to human bias and error, dependence on limited specialized expertise, and variability across interpreters. To address these challenges, we propose SpecXMaster, an intelligent framework leveraging Agentic Reinforcement Learning (RL) for NMR molecular spectral interpretation. SpecXMaster enables automated extraction of multiplicity information from both 1H and 13C spectra directly from r...
| Authors | Yutang Ge, Yaning Cui, Hanzheng Li et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 March 2026 |
| Last updated at source | 2 September 2026 |
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