Data-Driven Design of Transparent Thermal Insulating Nanoscale Layered Oxides

Abstract

Predicting the interfacial thermal resistance (ITR) for various material systems is a time-consuming process. In this study, we applied our previously proposed ITR machine learning models to discover the material systems that satisfy both high transparency and low thermal conductivity. The selected material system of TiO2/SiO2 shows a high ITR of 26.56 m2K/GW, which is in good agreement with the predicted value. The nanoscale layered TiO2/SiO2 thin films synthesized by sputtering exhibits ultralow thermal conductivity (0.21 W/mK) and high transparency (>90%, 380–800 nm). The reduction of the thermal conductivity is achieved by the high density of the interfaces with a high ITR rather than the change of the intrinsic thermal conductivity. The thermal conductivity of TiO2 is observed to be 1.56 W/mK with the film thickness in the range of 5–50 nm. Furthermore, the strong substrate dependence is confirmed as the thermal conductivity of the nanoscale layered TiO2/SiO2 thin films on quartz glass is three times lower than that on Si. The proposed TiO2/SiO2 composites have higher transparency and robustness, good adaptivity to electronics, and lower cost than the current transparent thermal insulating materials such as aerogels and polypropylene. The good agreement of the experimental ITR with the prediction and the low thermal conductivity of the layered thin films promise this strategy has great potential for accelerating the development of transparent thermal insulators.