  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.
