构建统一平台，推动光谱学深度学习研究标准化。
SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy
- 整合数据处理、评估工具与排行榜，形成统一研究平台。
- 生成14类任务、超10种光谱的多层基准，覆盖120万化学物质。
- 评测18个先进多模态模型，揭示现有方法关键局限。
深度学习在光谱学中潜力巨大，但当前研究缺乏统一标准。为此，我们提出SpectrumLab，一个开创性的统一平台，旨在系统化并加速光谱学中的深度学习研究。该平台包含三大核心组件：一个功能全面的Python库，集成基础数据处理与评估工具及排行榜；创新的SpectrumAnnotator模块，可从少量种子数据生成高质量基准；以及SpectrumBench，一个涵盖14项光谱任务、超过10种光谱类型、由逾120万不同化学物质筛选出的多层次基准套件。在SpectrumBench上对18个前沿多模态大模型进行充分实证研究，揭示了当前方法的关键局限。我们希望SpectrumLab能成为未来深度学习驱动光谱学发展的关键基础。
原文摘要 · Abstract (English)
Deep learning holds immense promise for spectroscopy, yet research and evaluation in this emerging field often lack standardized formulations. To address this issue, we introduce SpectrumLab, a pioneering unified platform designed to systematize and accelerate deep learning research in spectroscopy. SpectrumLab integrates three core components: a comprehensive Python library featuring essential data processing and evaluation tools, along with leaderboards; an innovative SpectrumAnnotator module that generates high-quality benchmarks from limited seed data; and SpectrumBench, a multi-layered benchmark suite covering 14 spectroscopic tasks and over 10 spectrum types, featuring spectra curated from over 1.2 million distinct chemical substances. Thorough empirical studies on SpectrumBench with 18 cutting-edge multimodal LLMs reveal critical limitations of current approaches. We hope SpectrumLab will serve as a crucial foundation for future advancements in deep learning-driven spectroscopy.
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