发表机构
The Hong Kong University of Science and Technology (Guangzhou); Jilin University; University of Auckland; Shanghai Artificial Intelligence Laboratory; Hunan University; The Hong Kong University of Science and Technology（香港科技大学（广州）; 吉林大学; 奥克兰大学; 上海人工智能实验室; 湖南大学; 香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对红外光谱化学传感的迁移难题，提出超1亿参数的红外光谱基础模型UltraIR，经6000万仿真光谱预训练后，在多类化学分析任务中性能优于基线，且适配性与数据效率优异。
AI 中文摘要
红外（IR）光谱技术被广泛应用于化学传感，但从光谱中提取可靠的化学信息仍具挑战性。传统的光谱解读方式费力、依赖先验知识和参考光谱，且难以规模化；而多数机器学习方法仅适配特定任务或数据集，需要大量标注训练样本，在不同分析目标和实验数据集间的迁移性较差。本文提出UltraIR，一款参数规模超1亿的红外光谱基础模型，可实现从分子到复杂样品的化学传感与分析的仿真到真实迁移学习。UltraIR通过光谱重构、分子指纹相似度对齐和官能团预测，在约6000万条仿真红外光谱上进行预训练，随后可通过任务特定标注或目标适配至下游任务。在官能团预测、分子结构解析、理化性质预测、混合物组分识别与定量、细菌分类、中药材产地溯源及成分定量、微塑料分类、土壤性质预测等任务中，UltraIR的性能优于传统机器学习及任务特定深度学习基线模型。该模型在标注实验光谱有限时表现优异，且在傅里叶变换红外光谱仪及实验室间的相同分析任务零样本推理中性能突出，为从复杂真实样品中实现适应性强、数据高效的化学传感提供了可行路径。
英文摘要
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.
