SpecXMaster Technical Report

TL;DR

SpecXMaster is an innovative AI framework that automates NMR spectral interpretation using reinforcement learning, reducing human bias and improving accuracy in chemical structure elucidation.

Contribution

It introduces a novel reinforcement learning-based method for fully automated NMR spectral interpretation directly from raw data.

Findings

Outperforms existing benchmarks in NMR interpretation accuracy

Enables automated extraction of multiplicity information from raw spectra

Refined through expert evaluations for high reliability

Abstract

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.