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SEMPro:一个数据驱动的管道,从扫描电子显微镜图像中学习结构属性洞察力

Brandon Ho¹, Jiayu Zhao², Joseph Liu²

¹Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, United States.

ACS materials letters

|November 16, 2023

概括

本研究介绍了SEMPro,这是一种深度学习工具,可以从扫描电子显微镜 (SEM) 图像中分析水凝微结构. SEMPro可以预测材料属性,并揭示结构-属性关系,从而推进水凝研究.

科学领域:

材料科学材料科学材料科学
生物材料工程生物材料工程
数据科学数据科学数据科学

背景情况:

通过扫描电子显微镜 (SEM) 进行水凝微观结构分析对于了解材料特性至关重要.
目前的方法依赖于主观解释和有限的数据集,阻碍了全面的结构-属性关系分析.

研究的目的:

开发一个数据驱动的解决方案,SEMPro,用于水凝SEM图像的自动分析.
使用深度学习 (DL) 建立一个管道来编译和分析水凝结构-属性关系.

主要方法:

网络抓取技术可以编译大量的水凝SEM图像数据集.
深度学习模型,包括转移学习和激活映射,用于图像分析和属性预测.
可解释的人工智能 (XAI) 方法来验证模型预测和解释特征相关性.

主要成果:

从SEM图像中,SEMPro可以准确地预测水凝的弹性模量,大小在相同的顺序内.
该模型成功地提取了模块相关的微结构特征,通过激活映射可视化.
可解释的人工智能证实了模型的预测有效性和特征重要性.

结论:

SEMPro提供了一个闭环系统用于水凝数据收集和分析,使得高维洞察力.
这种方法可以显著提高水凝和软材料的理解和设计.
DL和XAI的整合为材料科学研究提供了一个强大的工具.

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