Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging. Conventional interpretation is labor-intensive, relies on prior knowledge and reference spectra, and is difficult to scale, whereas most machine-learning methods are tailored to...
Yu-Sen Tan, Yixuan Chen, Zheng Fang et al.· 0 citations
Inferring molecular structures from infrared (IR) spectra is a fundamental yet challenging problem. A key difficulty is that an IR spectrum provides limited structural information: different molecules may share similar functional groups and local vibrational patterns, leading to highly similar spectral responses. Thus,...
Yixuan Chen, Bo Liu, Yu-Sen Tan et al.· arXiv.org· 0 citations
Infrared (IR) spectra provide characteristic signals of molecular structure, which are often interpreted by experts via functional-group identification or library matching, making the process time-consuming and ambiguous. Recent machine learning methods have made progress in molecular structure elucidation using molecu...
Yusen Tan, Hongyu Zhan, Hai-tao Yu et al.· 0 citations
Nuclear Magnetic Resonance spectroscopy is the gold standard for molecular structure elucidation, yet interpreting complex spectra for unknown molecules remains a bottleneck reliant on human expertise, so NMRAgent establishes a new paradigm for interpretable AI in analytical chemistry.
Zheng Fang, Yang Chen, Yusen Tan et al.· arXiv.org· 0 citations
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