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SpecXMaster Technical Report

Yutang Ge, Yaning Cui, Hanzheng Li, Jun-Jie Wang, Fanjie Xu, Jinhan Dong, Yongqi Jin, Dongxu Cui, Peng Jin, Guojiang Zhao, Hengxing Cai, Rong Zhu, Linfeng Zhang, Xiaohong Ji, Zhifeng Gao

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.

SpecXMaster Technical Report

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.
Paper Structure (49 sections, 42 equations, 4 figures, 4 tables)

This paper contains 49 sections, 42 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Full Processing Steps from FID to NMR Spectrum
  3. Overview
  4. System Architecture
  5. Signal Transformation
  6. Spectral Correction
  7. Multiplet Identification and Spectral Annotation
  8. Structured NMR Text Generation
  9. Agent Architecture
  10. Overview
  11. Problem Formulation
  12. Tool Environment
  13. Generate.
  14. Database search.
  15. Repair.
  16. ...and 34 more sections

Figures (4)

  • Figure 1: End-to-end pipeline of SpecXMaster
  • Figure 2: Overview of Nuclear Magnetic Resonance (NMR) Data Processing
  • Figure 3: Overview of SpecXMaster, the proposed agentic framework for NMR-based molecular structure elucidation. The agent iteratively constructs a decision state, selects actions over the tool environment, receives structured feedback, and updates the state until termination.
  • Figure 4: A comprehensive case study of elucidating molecular structure from FID data: (a) transformation of FID data into spectrum and multiplicity analysis; (b) agentic reasoning for structural elucidation; (c) reranking of candidate structures and final structure identification.