论文标题
通过混合对抗自动编码器和贝叶斯优化设计热辐射材料
Designing thermal radiation metamaterials via hybrid adversarial autoencoder and Bayesian optimization
论文作者
论文摘要
设计热辐射超材料是具有挑战性的，尤其是对于高度自由度和复杂目标的问题。在这封信中，我们开发了一种混合材料信息学方法，该方法结合了对抗性自动编码器和贝叶斯优化，以设计不同目标波长的窄带热发射器。只有数百个训练数据集，可以在压缩的二维潜在空间中快速弄清具有最佳特性的新结构。通过计算总候选结构的0.001 \％，这可以大大降低设计期间和成本，从而实现了最佳设计。提出的设计框架可以轻松地扩展到具有更高尺寸特征的其他热辐射超材料设计。
Designing thermal radiation metamaterials is challenging especially for problems with high degrees of freedom and complex objective. In this letter, we have developed a hybrid materials informatics approach which combines the adversarial autoencoder and Bayesian optimization to design narrowband thermal emitters at different target wavelengths. With only several hundreds of training data sets, new structures with optimal properties can be quickly figured out in a compressed 2-dimensional latent space. This enables the optimal design by calculating far less than 0.001\% of the total candidate structures, which greatly decreases the design period and cost. The proposed design framework can be easily extended to other thermal radiation metamaterials design with higher dimensional features.
