在液晶MHPOBC中对极子相的机器学习分类
Rebecca Betts1, Ingo Dierking1
1Department of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M139PL, UK. ingo.dierking@manchester.ac.uk.
Soft matter
|August 30, 2023
概括
卷积神经网络 (CNN) 从显微镜图像中准确地分类液晶相. 一个初始模型实现了93%的准确性,使材料科学应用的自动表征成为可能.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 人工智能的人工智能
背景情况:
- 液晶表现出不同的相 (SmA*,SmC*,SmC1/3*,SmCA*) 对于电光应用至关重要.
- 这些阶段的分类通常依赖于纹理图像的专家分析.
研究的目的:
- 开发和评估卷积神经网络 (CNN) 用于液晶相的自动分类.
- 在相位识别中比较顺序CNN和初始模型的性能.
主要方法:
- 用MHPOBC液晶相的实验极化显微镜图像作为训练数据.
- 两个CNN架构,顺序和初始模型,被训练为二进制和多相分类.
- 绩效是根据分类准确度来评估的.
主要成果:
- 两种CNN架构都成功地分类了液晶相.
- 最初的模型在多相分类中实现了高准确度 (93 ± 1) %.
- 顺序CNN在多相分类中实现了 (84±2) %的准确性.
结论:
- CNNs,特别是初始模型,为自动化液晶相位分类提供了高度准确的方法.
- 这种方法可以加速用于电光学,光学和传感器应用的新型液晶的表征.
- 机器学习程序可以适应各种液晶材料和相.
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