基于神经网络的液晶张量模型,具有分子级信息的液晶
Baoming Shi1, Apala Majumdar2, Lei Zhang3
1Peking University, School of Mathematical Sciences, Beijing 100871, China.
Physical review. E
|February 20, 2026
概括
与传统模型相比,用于液晶 (LC) 的新神经网络张量 (NN-tensor) 模型提供了更高的准确性和相位过渡预测. 这种高效的框架准确地计算了多个阶段的复杂LC微结构.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 软物质物理学 软物质物理学
背景情况:
- 兰道-德热内斯 (LdG) 模型是液晶 (LC) 阶段的连续理论.
- 与分子级模拟相比,LdG模型的准确性和物理信息较低.
- 精确的LC相建模对于材料科学和设备应用至关重要.
研究的目的:
- 为液晶开发一种基于神经网络的新型张量 (NN-tensor) 模型.
- 为了提高LC相模拟的精度和物理准确性.
- 为了高效计算稳定的LC配置,并解决复杂的微观结构.
主要方法:
- 开发了一个由底层分子模型监督的NN-tensor模型.
- 将NN-tensor模型集成到第二个神经网络中,以实现高效的计算.
- 验证了 nematic 和 smectic 液晶相的模型.
主要成果:
- 该NN-tensor模型实现了与分子模型可比的能量精度.
- 它准确地捕获了同otropic-nematic 阶段过渡,超过了 LdG 模型.
- 该模型量化预测了涂层厚度,并解决了Omega和T形粒边界等复杂的微观结构.
结论:
- 该NN-tensor框架为计算LC配置提供了一个统一,高效和物理忠实的方法.
- 这种方法克服了传统方法在解决复杂的LC微结构方面的局限性.
- 这种NN-tensor模型在模拟各种液晶相方面取得了重大进展.
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