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用人工神经网络预测液晶行为

Sarah Chattha1, Simant R Upreti1, Philip K Chan1

  • 1Department of Chemical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada.

Micromachines
|December 31, 2025
PubMed
概括

人工神经网络 (ANN) 准确地预测液晶 (LC) 属性,如极角和折射率. 这为优化LC设备的复杂模拟提供了更快的替代方案.

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算物理 计算物理
  • 光电学是指光电子产品.

背景情况:

  • 液晶 (LC) 具有流体和固体特性,对于显示器和传感器至关重要.
  • 准确预测LC属性,如极角和折射率,对于设备优化至关重要.
  • 传统的建模方法是计算密集型的,需要有效的替代方案.

研究的目的:

  • 开发人工神经网络 (ANN) 来预测LC平均平稳极角和折射率.
  • 评估ANN性能与LC建模的传统模拟方法相比.
  • 探索ANN作为一种低延迟工具,用于优化基于LC的技术.

主要方法:

  • 人工神经网络 (ANN) 经过训练,可以从表面粘度和定能量预测LC的特性.
  • 列车,验证,测试方法被用来评估预测准确性.
  • 采用K折交叉验证来进一步验证ANN模型的性能.

主要成果:

  • ANN模型显示出高的预测准确性,ANN_A4 (R2 = 0.9995) 和ANN_B2 (R2 = 0.9969) 通过训练,验证和测试方法显示出出色的结果.
  • K-Fold交叉验证揭示了不同的最佳模型,其中ANN_A5* (R2 = 0.40767) 和ANN_B4* (R2 = 0.93799) 的表现最好.
关键词:
的 LCs 的 LCs.人工智能的人工智能是人工智能.人工神经网络的人工神经网络

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  • 与传统的模拟技术相比,ANN的延迟时间显著降低.

    • 结论:

    • 在模拟液晶方面,ANN具有巨大的潜力,可以准确预测关键光学特性.
    • 由于ANN的低延迟,它们适合于LC技术中的计算密集型优化任务.
    • 这项研究强调了ANN作为推进液晶应用的强大和高效工具.