SpecXMaster Technical Report
This addresses the need for automated and reliable NMR spectral interpretation in organic chemistry, reducing reliance on human expertise and errors.
The paper tackles the problem of expert-dependent spectral interpretation in NMR spectroscopy, which suffers from human bias and variability, by proposing SpecXMaster, an intelligent framework using Agentic Reinforcement Learning for automated extraction of multiplicity information from raw FID data, achieving superior performance on public benchmarks.
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 raw FID (free induction decay) data. This end-to-end pipeline enables fully automated interpretation of NMR spectra into chemical structures. It demonstrates superior performance across multiple public NMR interpretation benchmarks and has been refined through iterative evaluations by professional chemical spectroscopists. We believe that SpecXMaster, as a novel methodological paradigm for spectral interpretation, will have a profound impact on the organic chemistry community.