arXiv Machine Learning July 21, 2026 By Yutang Ge, Yaning Cui, Hanzheng Li, Jun-Jie Wang, Fanjie Xu, Jinhan Dong, Yongqi Jin, Dongxu Cui, Peng Jin, Guojiang Zhao, Hengxing Cai, Tianci Yangfeng, Xueqing Chen, Hongshuai Wang, Rong Zhu, Linfeng Zhang, Xiaohong Ji, Zhifeng Gao

SpecXMaster Technical Report

Read the original on arXiv Machine Learning →

arXiv:2603. 23101v3 Announce Type: replace 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 23

NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem

arXiv:2607. 19406v1 Announce Type: new Abstract: Structural elucidation from Nuclear Magnetic Resonance (NMR) data remains a fundamental bottleneck across chemistry, materials science, and biology.

By Irina Espejo Morales, Damon Hinz, Marvin Alberts, Geraud Krawezik, Haewon Jeong, Shirley Ho

llms agents

arXiv Machine Learning

Sep 1

S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation

arXiv:2608.30910v1 Announce Type: new Abstract: Spectroscopic structure elucidation is central to molecular analysis, but recent Large Language Model (LLM)-based methods mostly formulate it as direct...

By Xuanle Zhao, Xinyuan Cai, Xiang Cheng, Bo Xu

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 18

Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra

arXiv:2608. 14720v1 Announce Type: cross Abstract: Following the molecular discovery and synthesis revolutions, scalable automated structure elucidation from routine spectroscopic data remains an outstanding challenge.

By Bingsen Xue, Zhuojun Jiang, Jianhao Zhang, Mingcheng Gu, Yizhe Yuan, Yongtai Zhuo, Yifan Zhang, Li Wang, Ya Su, Yue Yuan, Jiang Liu, Xueqian Kong, Cheng Jin

agents multimodal

arXiv Machine Learning

Jul 14

NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectra

arXiv:2512. 19733v3 Announce Type: replace-cross Abstract: Molecular structure elucidation from spectroscopic data is a long-standing challenge in Chemistry, traditionally requiring expert interpretation.

By Federico Ottomano, Yingzhen Li, Alex M. Ganose

rag fine-tuning multimodal

arXiv Machine Learning

Sep 10

From Human Labels to Literature: Semi-Supervised Learning of NMR Chemical Shifts at Scale

The paper introduces a semi‑supervised framework that learns to predict nuclear magnetic resonance (NMR) chemical shifts from millions of literature‑extracted spectra without explicit atom‑level assignments. By treating the prediction as a permutation‑invariant set supervision problem, the authors show that optimal bipartite matching can be reduced to a sorting‑based loss, enabling stable large‑scale training. The resulting models outperform state‑of‑the‑art methods, generalize better to diverse molecules, and for the first time capture systematic solvent effects across common NMR solvents.

By Yongqi Jin, Yecheng Wang, Jun-jie Wang, Rong Zhu, Guolin Ke, Weinan E

benchmarks

arXiv Machine Learning

Jun 10

Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence

arXiv:2512. 18531v2 Announce Type: replace-cross Abstract: One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products.

By Frank Hu, Jonathan M. Tubb, Dimitris Argyropoulos, Sergey Golotvin, Mikhail Elyashberg, Grant M. Rotskoff, Matthew W. Kanan, Thomas E. Markland

llms nlp fine-tuning

One email a morning, machine-written

Related stories

NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem

S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation

Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra

NMIRacle: Multi-modal Generative Molecular Elucidation from IR and NMR Spectra

From Human Labels to Literature: Semi-Supervised Learning of NMR Chemical Shifts at Scale

Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence