UltraIR:从分子到复杂样品的红外光谱化学感知与分析仿真到现实迁移学习 [预印本]
该论文主题为红外光谱化学感知与分析，属于化学与机器学习交叉领域，与农业信息化、智慧农业等主题无直接关联，不满足相关性门槛。
中国作者团队发布1 亿参数红外光谱基础模型 UltraIR,基于约 6,000 万模拟光谱预训练并融合谱图重建、分子指纹对齐与官能团预测;在官能团预测、混合物定量、细菌分类、药材产地鉴别、微塑料分类及土壤属性预测等任务上均超越传统机器学习与任务专用深度学习,少样本与跨实验室零样本迁移能力显著,为农业土壤属性检测、药材溯源与农产品品质化学感知提供数据高效新路径。[预印本,未经同行评审]
Authors:Yusen Tan, Yixuan Chen, Zheng Fang, Pan Liu, Yifan Li, Qinyu Guo, Zhedong Lin, Yuqiang Li, Xiangxiang Zeng, Tong Wang, Jun Xia
Abstract: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 individual tasks or datasets, require large labeled training sets, and transfer poorly across analytical objectives and experimental datasets. Here we introduce UltraIR, a foundation model for IR spectroscopy with more than 100 million parameters that enables simulation-to-real transfer learning for chemical sensing and analysis from molecules to complex samples. UltraIR is pretrained on approximately 60 million simulated IR spectra using spectral reconstruction, molecular fingerprint similarity alignment, and functional-group prediction, then adapted to downstream objectives with task-specific labels or targets. Across functional-group prediction, molecular structure elucidation, physicochemical property prediction, mixture-component identification and quantification, bacterial classification, medicinal-herb geographic origin traceability and constituent quantification, microplastics classification, and soil property prediction, UltraIR outperforms conventional machine-learning and task-specific deep-learning baselines. It performs strongly with limited labeled experimental spectra and in zero-shot inference for the same analytical task across Fourier-transform infrared spectrometers and laboratories, providing a route to adaptable, data-efficient chemical sensing from complex real-world samples.
Subjects:Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:arXiv:2608.13341 [cs.LG]
(or arXiv:2608.13341v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.13341
arXiv-issued DOI via DataCite
Submission history
From: Yusen Tan [view email]
[v1] Thu, 13 Aug 2026 15:11:50 UTC (7,895 KB)
[v2] Fri, 14 Aug 2026 03:01:37 UTC (7,895 KB)