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机器学习用于分析材料化学中的实验散射和光谱数据
Andy S Anker1, Keith T Butler2, Raghavendra Selvan3,4
1Department of Chemistry and Nano-Science Center, University of Copenhagen 2100 Copenhagen Ø Denmark kirsten@chem.ku.dk.
Chemical science
|December 15, 2023
概括
机器学习 (ML) 加快了材料化学数据分析,克服了传统方法的瓶. 无监督的ML为挑战提供解决方案,增强实验特征和指导新研究人员.
科学领域:
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
- 计算化学计算化学
背景情况:
- 来自先进设施的材料化学数据的快速增长超过了传统分析.
- 数据分析和建模中的手动努力是一个重要的瓶.
- 机器学习 (ML) 为高效的数据解释提供了潜在的解决方案.
研究的目的:
- 调查材料化学中的分散和光谱数据分析的监督和无监督ML.
- 在粉末衍射 (PD),对分布函数 (PDF),小角度散射 (SAS),不弹性中子散射 (INS) 和X射线吸收光谱 (XAS) 中审查ML应用.
- 突出ML的潜力,以提高实验性特征和指导新人.
主要方法:
- 监督和无监督机器学习技术的应用.
- 专注于散射和光谱学数据的分析 (PD,PDF,SAS,INS,XAS).
- 审查和讨论ML的能力和局限性.
主要成果:
- 机器学习可以从实验数据中高效准确地识别物理和结构模型.
- 无监督ML减轻了与监督ML相关的限制.
- 通过ML,可以实现改进的实验材料化学数据分析.
结论:
- 在材料化学表征方面,ML具有变革性的潜力.
- 无监督的ML对于克服监督ML的挑战至关重要.
- 这种观点引导研究人员采用ML进行实验数据分析.
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