SpecXMaster Technical Report

2026-03-24

AI Summary

Traditional nuclear magnetic resonance (NMR) spectral interpretation heavily relies on expert knowledge, making it susceptible to subjective bias and constrained by the scarcity of skilled practitioners. This work proposes SpecXMaster, a novel framework that introduces reinforcement learning–based intelligent agents into NMR analysis for the first time, establishing an end-to-end deep learning architecture capable of directly extracting multiplicity information from raw free induction decay (FID) signals for both ¹H and ¹³C spectra and subsequently inferring molecular structures. The method operates without manual intervention or predefined rules, and through iterative refinement guided by multiple rounds of evaluation from expert chemists, achieves high-accuracy fully automated interpretation across several public benchmarks. Its performance has been validated and endorsed by professional spectroscopists, marking a significant advance beyond the limitations of conventional paradigms.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Structured Learning
• Multiagent Systems: Multiagent Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• spectral interpretation
• NMR spectroscopy
• expert-dependent analysis
• human bias
• interpretation variability

Innovation

• Agentic Reinforcement Learning
• NMR spectral interpretation
• end-to-end automation
• FID data processing
• chemical structure elucidation