论文标题
使用人工神经网络和遗传算法的多层纳米颗粒的逆设计
Inverse design of multilayer nanoparticles using artificial neural networks and genetic algorithm
论文作者
论文摘要
多层纳米颗粒的光散射可以通过Maxwell方程来解决。但是，很难使用传统的试用和错误方法来解决多层纳米颗粒的反设计。在这里，我们提出了一种转发模拟和多层纳米颗粒的逆设计的方法。我们将遗传算法的全球搜索能力与神经网络的局部搜索能力相结合。首先，遗传算法用于找到合适的溶液，然后使用神经网络对其进行微调。由于物理结构与光学响应之间的非唯一关系，我们首先训练一个正向神经网络，然后将其应用于多层纳米颗粒的逆设计。不仅在这里，此方法可以轻松扩展以预测和找到其他光学结构的最佳设计参数。
The light scattering of multilayer nanoparticles can be solved by Maxwell equations. However, it is difficult to solve the inverse design of multilayer nanoparticles by using the traditional trial-and-error method. Here, we present a method for forward simulation and inverse design of multilayer nanoparticles. We combine the global search ability of genetic algorithm with the local search ability of neural network. First, the genetic algorithm is used to find a suitable solution, and then the neural network is used to fine-tune it. Due to the non-unique relationship between physical structures and optical responses, we first train a forward neural network, and then it is applied to the inverse design of multilayer nanoparticles. Not only here, this method can easily be extended to predict and find the best design parameters for other optical structures.
