|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Late News Poster Session
||M-25: AI-based Design of Daytime Radiative Cooling Materials with Record Temperature Reduction
||Quang-Tuyen Le, Sih-Wei Chang, Huyen-Anh Thi Phan, An-Chen Yang, Nan-yow Chen, Hsueh-Li Chen, Yu-Chieh Lo, Dehui Wan
|On-Site Speaker (Planned)
In this work, we propose an AI-based deep generative model combined with a one-dimensional convolutional neural network to performs the inverse design process of the extraordinary passive daytime radiative cooling materials in a probabilistic way. The AI-enabled strategy really delivers a comprehensive solution for the one-to-many mapping problem of inverse design. The prediction results are then validated by Kramers-Kronig relations and Lorentz-Drude model, and a new record-breaking PDRC material with a reduction of ~79 K compared with ambient temperature, and ~12 K lower than the temperature of the conventional ideal selective emitter is discovered. The AI-extrapolated extraordinary PDRC materials provide a new guideline for designing PDRC materials in the future and connect the gap between ideal selective emitter and real materials.
||Machine Learning, Thin Films and Interfaces, Modeling and Simulation