arXiv Machine Learning
Jul 21

SpecXMaster Technical Report

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.

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
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
Hugging Face Trending Papers
Jun 29

Towards Generalizable and Evidential Nuclear Magnetic Resonance-Based Molecular Structure Elucidation via Large Language Model Agent

Nuclear Magnetic Resonance (NMR) spectroscopy is the gold standard for molecular structure elucidation, yet interpreting complex spectra for unknown molecules remains a bottleneck reliant on human expertise. While artificial intelligence has advanced this field, current methods face a critical trade-off: database retrieval cannot identify novel scaffolds, while de novo molecular structure elucidation models operate as black boxes, lacking the atom-level interpretability required for rigorous scientific validation.

arXiv Machine Learning
Oct 5

Explainable Molecular Structure Inference from GC--MS with Diffusion Models and LLM Reranking

The paper introduces DiffGCMS, a discrete graph diffusion model that generates molecular structures directly from GC–EI–MS spectra, and couples it with a large language model for post‑processing. In the first stage, DiffGCMS produces candidate structures; the LLM then validates, repairs, and reranks these candidates while offering interpretable explanations of fragment‑ion peaks. On a large NIST 20 test set, the combined approach improves accuracy and ensures 100% candidate validity for small molecules, demonstrating the value of spectrum‑aware post‑processing.

By Changlin Liu, Tianyu Yi, Chengchun Liu, Boxuan Zhao, Fanyang Mo