通过机器学习区分液晶内马特变体
Alexander R Quinn1, Rebecca Walker2, Naila Tufaha2
1Department of Physics and Astronomy, University of Manchester, Oxford Road, Manchester, M13 9PL, UK. ingo.dierking@manchester.ac.uk.
Soft matter
|December 8, 2025
概括
机器学习模型准确地区分液晶相. 具有翻转增强的3层卷积神经网络 (CNN) 达到超过0.96的准确性,证明有效地识别阴性变异.
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
- 机器学习 机器学习
背景情况:
- 液晶表现出各种各样的相位,包括铁电和扭曲曲线.
- 区分这些相对于理解它们的特性和应用至关重要.
- 机器学习为自动化阶段识别提供了潜力.
研究的目的:
- 评估序列卷积神经网络 (CNN) 和并行开始模型,以识别阴性液晶变体.
- 为了确定最佳的模型复杂性和数据增强策略,以准确地进行相位分类.
- 评估不同机器学习架构在区分铁电和扭曲曲内马特相的性能.
主要方法:
- 在液晶数据上训练CNN (1-5层) 和初始模型 (1-3块).
- 应用数据增强技术,包括翻转,对比度和亮度.
- 集成的脱落层规范化,以防止过.
- 系统地分析了每个模型配置的准确性和错误率.
主要成果:
- 翻转增强显著提高了准确性,而掉队调整通常会降低准确性.
- 一个三层CNN或一个单一的初始块模型实现了0.96-0.98±0.01的准确度与翻转增强.
- 序列CNN足以对多达四个内马特相的序列进行分类.
- 接近100%的更高准确度可以通过更大,类平衡的数据集实现.
结论:
- 机器学习模型,特别是三层CNN,可以可靠地区分阴性液晶变体.
- 翻转增强是提高分类准确性的关键技术.
- 对于具有四个或更少阶段的数据集,顺序CNN提供了一个计算效率高的解决方案.
- 初始模型可能为更大的数据集提供好处,只要管理过拟合.
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