SpecXMaster Technical Report
cs.LG
Submitted: 2026-03-24
Updated: 2026-09-02
Comments: Technical report from DP Technology.22 pages, 7 figures
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: 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.
Terminology
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.
Sources
- From Human Labels to Literature: Semi-Supervised Learning of NMR Chemical Shifts at Scale
- SpectraLLM: Uncovering the Ability of LLMs for Molecular Structure Elucidation from Multi-Spectral Data
- SpecMol: A Spectroscopy-Grounded Foundation Model for Multi-Task Molecular Learning
- NMR-Solver: Automated Structure Elucidation via Large-Scale Spectral Matching and Physics-Guided Fragment Optimization
- MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning
- NMRPeak: a ready-to-use intelligent system for molecular structure elucidation enabled by synergistic cross-modal learning
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs
- Encouraging Good Processes Without the Need for Good Answers: Reinforcement Learning for LLM Agent Planning
- Qwen2.5 Technical Report
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