机器学习增强的试错用于高效优化复合材料的优化
Wei Deng1, Lijun Liu1, Xiaohang Li1
1State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, 130022, P. R. China.
Advanced materials (Deerfield Beach, Fla.)
|March 10, 2025
概括
优化复合材料现在更高效,有了新的机器学习 (ML) 增强的试错方法. 这种方法整合了实验设计和符号回归 (SR) 以更快地优化材料属性.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 优化复合材料的传统试错方法是低效的.
- 现有的机器学习 (ML) 方法因处理条件依赖性而难以进行属性预测,阻碍了数据集成.
研究的目的:
- 开发一种新的,高效的工作流程,以优化复合材料的性能.
- 克服传统和当前ML辅助优化方法的局限性.
主要方法:
- 将直角实验设计与符号回归 (SR) 集成在一起,以创建一个ML增强的试错方法.
- 使用复合材料作为模型系统来验证工作流程.
- 开发一个在线,无代码的平台,用于方法的实施.
主要成果:
- 用ML增强的试错方法有效地从实验数据中提取经验原则.
- 在SR衍生公式中的高频术语为材料性能优化提供了明确的指导.
- 拟议的工作流显著提高了优化流程的效率和能力.
结论:
- 用ML增强的试错方法为优化复合材料提供了强大的,高效的替代方案.
- 该方法成功地提取了指导实证原则,增强了预测和优化能力.
- 开发的在线平台有助于无整合到实验工作流程中.
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