演机器学习在化学应用中的挑战和机遇
1Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA;
Annual review of chemical and biomolecular engineering
|April 10, 2024
概括
机器学习在数据驱动模型方面表现出色,但在演推理方面却扎不堪. 本综述探讨了化学科学和工程方面的专家干预,提出了能够进行推断的机器学习模型.
科学领域:
- 化学科学与工程 化学科学与工程
- 计算机科学 计算机科学
背景情况:
- 现代机器学习自动化了从数据中开发数学模型.
- 在化学科学和工程中,专家的干预对于演推理任务至关重要.
研究的目的:
- 分析专家干预在演推理中的作用.
- 识别创造演瓶的因素.
- 提出能够进行推断的机器学习模型.
主要方法:
- 对演推理特征的审查.
- 分析专家在解决问题的干预.
- 讨论目前对演瓶的方法.
- 演机器学习模型的设计原则.
主要成果:
- 演推理对标准的机器学习策略具有抗性.
- 在需要演推理的情况下,专家的干预至关重要.
- 确定了导致演瓶的因素.
结论:
- 机器学习需要结合演能力.
- 解决演瓶可以提高科学和工程问题的解决.
- 教程案例研究和笔记本提供了实践探索.
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