人工智能,机器学习和化学测量的趋势,应用于化学数据
Rola Houhou1,2, Thomas Bocklitz1,2
1Institute of Physical Chemistry Friedrich-Schiller-University Jena Jena Germany.
Analytical science advances
|May 8, 2024
概括
化学计量,机器学习和深度学习等人工智能 (AI) 方法可以增强化学数据分析. 本综述强调了化学和光谱数据的AI的2020年趋势,最大限度地提取信息和应用可能性.
科学领域:
- 分析化学 分析化学
- 数据科学数据科学数据科学
- 计算化学计算化学
背景情况:
- 人工智能 (AI) 方法,包括化学测量,机器学习 (ML) 和深度学习 (DL),为数据解释提供了强大的工具.
- 这些人工智能技术能够全面利用数据,最大限度地了解过程,相互作用和样本特征.
- 人工智能在化学数据分析中的应用可以追溯到20世纪70年代,近年来不断进步.
研究的目的:
- 审查基于人工智能的化学和光谱数据分析方法的最新趋势,重点关注2020年的发展.
- 探索化学测量,机器学习和深度学习的应用,从化学数据集中提取信息.
- 通过先进的AI技术,讨论化学数据的增强应用可能性.
主要方法:
- 专注于从2020年起应用到化学和光谱数据的化学测量,机器学习和深度学习的最新趋势.
- 讨论化学数据分析的反向建模技术.
- 检查各种测量技术中的光谱和图像数据的数据预处理和建模策略.
主要成果:
- 基于人工智能的方法显著提高了化学数据的理解和利用.
- 最近的趋势表明,用于光谱和图像数据的ML和DL应用程序的复杂性增加了.
- 有效的预处理和反向建模对于从复杂的化学数据集中最大限度地获取信息至关重要.
结论:
- 化学测量,机器学习和深度学习对于现代化学数据分析至关重要.
- 人工智能工具能够更准确和自动地提取信息,扩大化学数据的实用性.
- 对化学人工智能的持续研究有望在理解和应用方面取得进一步的突破.
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