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全介电芯纳米结构的机器学习:在反向设计中对象函数的关键作用
David J Hoxie1, Purushotham V Bangalore2, Kannatassen Appavoo1
1Department of Physics, University of Alabama at Birmingham, Birmingham, AL 35294, USA. appavoo@uab.edu.
Nanoscale
|November 20, 2023
概括
选择正确的目标函数是利用机器学习优化纳米光子设备的关键. 不同的目标函数在不同训练数据的神经网络中表现最好,影响纳米光子设计的准确性.
科学领域:
- 纳米光子学 纳米光子学
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
背景情况:
- 将纳米光子学集成到基于光的技术中,需要对纳米共振器光学反应进行精确的控制.
- 纳米共振器的光学共振对材料组成和结构参数非常敏感.
- 机器学习 (ML) 为优化纳米光子设计提供了潜力,但在探索广的参数空间方面面临着挑战.
研究的目的:
- 在使用训练有素的神经网络时,研究目标函数的选择如何影响纳米光子学中的优化过程.
- 评估客观函数和神经网络"经验" (训练数据比例) 对准确识别所需光学反应的纳米结构参数的影响.
主要方法:
- 使用核心外,全介电纳米结构作为评估光学Mie响应的基准.
- 员工训练有素的神经网络与各种客观功能相结合,以优化结构参数.
- 根据提供给神经网络的训练数据量来量化不同目标功能的性能.
主要成果:
- 对象函数的选择显著改变了纳米光子结构的优化轨迹和结果.
- 通过在广泛的数据集 ("经验丰富"网络) 上训练的神经网络,某些目标函数的准确性更高.
- 意想不到的是,其他客观功能在训练较少 ("缺乏经验") 的神经网络中表现优异.
结论:
- 了解神经网络和优化方案之间的相互作用对于有效的纳米光子设计至关重要.
- 在纳米光子学中,ML驱动优化的有效性严重依赖于客观功能的明智选择和神经网络的训练.
- 这些发现甚至可以用简单的核心纳米结构来证明,这突显了这种关系的基本重要性.
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