通过深度学习预测的无otropic凝聚物质中的自发纹崩
Kitae Kim1, Jun-Hee Na1,2
1Department of Convergence System Engineering, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, Republic of Korea.
Small (Weinheim an der Bergstrasse, Germany)
|November 25, 2025
概括
一个新的深度学习框架可以快速预测液晶配置. 这种人工智能模型准确地捕捉了分子秩序和缺陷,加速了材料科学研究.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
背景情况:
- 阴性液晶表现出复杂的方向顺序和拓缺陷.
- 预测这些配置通常需要计算密集型模拟.
- 控制拓纹理对于先进的光学应用至关重要.
研究的目的:
- 开发一个快速而准确的深度学习框架,用于预测阴性液晶配置.
- 通过实验观察来验证模型的预测.
- 为了建立一个可通用的数据驱动的替代体为内马特系统.
主要方法:
- 一个3D U-Net深度学习模型被训练在来自有限元素Landau-de Gennes解决器的数据上.
- 模拟的导演场与来自光对齐纹基板的实验数据进行了比较.
- 极化光学显微镜 (POM) 图像用于验证.
主要成果:
- 3D U-Net 模型准确地预测了全球定向顺序和局部缺陷结构.
- 预测是在毫秒内生成的,比传统的模拟要快得多.
- 该模型成功地重现了复杂的缺陷行为,包括崩和分裂.
- 实验验证证证实了该模型在各种边界条件的可靠性和忠实性.
结论:
- 深度学习框架提供了一个强大的,数据驱动的替代品来模拟阴性液晶.
- 这种方法是计算理论和实验验证之间的桥梁.
- 它为设计和控制光子学和光学材料中的拓纹理提供了一条途径.
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