Designing thermal radiation metamaterials via a hybrid adversarial autoencoder and Bayesian optimization
Designing thermal radiation metamaterials is challenging especially for problems with high degrees of freedom and complex objectives. In this Letter, we develop a hybrid materials informatics approach which combines the adversarial autoencoder and Bayesian optimization to design narrowband thermal e...
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| Vydáno v: | Optics letters Ročník 47; číslo 14; s. 3395 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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15.07.2022
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| ISSN: | 1539-4794, 1539-4794 |
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| Abstract | Designing thermal radiation metamaterials is challenging especially for problems with high degrees of freedom and complex objectives. In this Letter, we develop 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 determined in a compressed two-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.Designing thermal radiation metamaterials is challenging especially for problems with high degrees of freedom and complex objectives. In this Letter, we develop 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 determined in a compressed two-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. |
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| AbstractList | Designing thermal radiation metamaterials is challenging especially for problems with high degrees of freedom and complex objectives. In this Letter, we develop 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 determined in a compressed two-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.Designing thermal radiation metamaterials is challenging especially for problems with high degrees of freedom and complex objectives. In this Letter, we develop 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 determined in a compressed two-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. |
| Author | Zhu, Dezhao Yu, Gang Ju, Shenghong Wang, Hong Guo, Jiang Zhao, C Y |
| Author_xml | – sequence: 1 givenname: Dezhao surname: Zhu fullname: Zhu, Dezhao – sequence: 2 givenname: Jiang surname: Guo fullname: Guo, Jiang – sequence: 3 givenname: Gang surname: Yu fullname: Yu, Gang – sequence: 4 givenname: C Y surname: Zhao fullname: Zhao, C Y – sequence: 5 givenname: Hong surname: Wang fullname: Wang, Hong – sequence: 6 givenname: Shenghong surname: Ju fullname: Ju, Shenghong |
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| CitedBy_id | crossref_primary_10_1016_j_ijheatmasstransfer_2023_124831 crossref_primary_10_1016_j_solmat_2024_112822 crossref_primary_10_1016_j_optcom_2024_130569 crossref_primary_10_1002_advs_202401951 crossref_primary_10_1016_j_ijheatmasstransfer_2022_123332 crossref_primary_10_1016_j_ijmecsci_2025_110335 crossref_primary_10_1016_j_nxener_2023_100078 crossref_primary_10_1063_5_0250763 crossref_primary_10_1515_nanoph_2025_0159 crossref_primary_10_1364_AO_465157 crossref_primary_10_34133_icomputing_0135 |
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