机器学习光谱学以推进计算和分析
Julia Westermayr1,2, P Marquetand3
1Wilhelm-Ostwald-Institut für Physikalische und Theoretische Chemie, Universität Leipzig Linnéstraße 2 04103 Leipzig Germany julia.westermayr@uni-leipzig.de.
Chemical science
|November 10, 2025
概括
机器学习 (ML) 增强了计算光谱,但需要更多地关注实验数据. 本综述探讨了ML和光谱学的协同作用,用于自动化从光谱的结构和组成预测.
科学领域:
- 化学 化学 化学
- 材料科学 材料科学 材料科学
- 生物学 生物学 生物学
- 医学 医学 医学 医学 医学
背景情况:
- 光谱学分析物质-辐射相互作用,用于样本的表征.
- 机器学习 (ML) 通过高效的预测和数据生成,推进了理论光谱学.
- ML在实验光谱数据处理中的应用仍未得到充分探索.
研究的目的:
- 审查机器学习和光谱学之间的协同作用.
- 涵盖各种光谱技术 (光学,X射线,NMR,质谱学).
- 概述ML的基本原理和该领域的未来发展.
主要方法:
- 审查现有的关于ML在光谱学中的应用文献.
- 讨论与光谱数据分析相关的ML技术.
- 计算和实验光谱学集成的探索.
主要成果:
- ML已经显著改善了理论光谱学.
- 机器学习在处理实验光谱数据方面存在着巨大的,但尚未被充分探索的潜力.
- 为结构和组成预测自动化光谱分析是一个关键的挑战.
结论:
- 机器学习与实验光谱学的整合具有很大的前景.
- 需要进一步的研究才能充分利用ML来分析光谱数据.
- 这种协同作用可以推进化学,材料科学和医学等领域.
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