SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy

Authors:
  • ZYZhuo Yang
    Zhuo Yang
  • JXJiaqing Xie
    Jiaqing Xie
  • SSShuaike Shen
    Shuaike Shen
  • DWDaolang Wang
    Daolang Wang
  • YCYeyun Chen
    Yeyun Chen
  • BGBen Gao
    Ben Gao
  • SSShuzhou Sun
    Shuzhou Sun
  • BQBiqing Qi
    Biqing Qi
  • DZDongzhan Zhou
    Dongzhan Zhou
  • LBLei Bai
    Lei Bai
  • LCLinjiang Chen
    Linjiang Chen
  • SZShufei Zhang
    Shufei Zhang
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Abstract
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.