Northwest Institute of Mechanical & Electrical Engineering, Xianyang, Shaanxi, China.
School of Optoelectronic Engineering, Xidian University, Xi'an, China.
PLoS One. 2024 May 2;19(5):e0302262. doi: 10.1371/journal.pone.0302262. eCollection 2024.
The yolk shell is widely used in optoelectronic devices due to its excellent optical properties. Compared to single metal nanostructures, yolk shells have more controllable degrees of freedom, which may make experiments and simulations more complex. Using neural networks can efficiently simplify the computational process of yolk shell. In our work, the relationship between the size and the absorption efficiency of the yolk-shell structure is established using a backpropagation neural network (BPNN), significantly simplifying the calculation process while ensuring accuracy equivalent to discrete dipole scattering (DDSCAT). The absorption efficiency of the yolk shell was comprehensively described through the forward and reverse prediction processes. In forward prediction, the absorption spectrum of yolk shell is obtained through its size parameter. In reverse prediction, the size parameters of yolk shells are predicted through absorption spectra. A comparison with the traditional DDSCAT demonstrated the high precision prediction capability and fast computation of this method, with minimal memory consumption.
蛋黄壳由于其优异的光学性能而被广泛应用于光电器件中。与单一的金属纳米结构相比，蛋黄壳具有更多的可控自由度，这可能使实验和模拟更加复杂。使用神经网络可以有效地简化蛋黄壳的计算过程。在我们的工作中，使用反向传播神经网络（BPNN）建立了蛋黄壳结构的尺寸和吸收效率之间的关系，在保证与离散偶极子散射（DDSCAT）相当的准确性的同时，大大简化了计算过程。通过正向和反向预测过程，全面描述了蛋黄壳的吸收效率。在正向预测中，通过其尺寸参数获得蛋黄壳的吸收光谱。在反向预测中，通过吸收光谱预测蛋黄壳的尺寸参数。与传统的 DDSCAT 相比，该方法具有高精度预测能力和快速计算能力，且内存消耗最小。
