CDS&E: First Principles Prediction of Thermal Radiative Properties of Dielectric Materials

This project is funded by the Condensed-Matter-and-Materials-Theory program in the Division of Materials Research and by the programs in Computational and Data-Enabled Science and Engineering and Thermal Transport Processes in the Division of Chemical, Bioengineering, Environmental, and Transport Systems.

Non-technical summary

Thermal radiation plays a key role in a broad set of energy and thermal-management applications, including spacecraft, solar cells, and passive radiative cooling. These applications often require distinct selective radiative properties: high absorption of sunlight is needed for solar cells, while low absorption of sunlight and high emission of infrared light in the window of atmospheric transparency are desired for radiative cooling.

By reflecting sunlight while radiating infrared light to space, radiative-cooling paints have been shown to cool surfaces to below the ambient temperature without any energy expenditure. Screening and designing such materials call for an understanding of how thermal radiative properties depend on the atomic structures of materials. However, methods and software tools for this purpose are generally lacking, and empirical trial-and-error approaches are still the mainstream.

Therefore, the objectives of this project are to enhance theoretical and simulation methodologies that can predict thermal radiative properties of materials from their atomic structures and subsequently to develop and deploy an open-source code that will help other researchers model their own radiative materials. Moreover, the PI will use these tools to understand the atomistic origins of ultra-efficient radiative cooling in particle-matrix nanocomposites and employ machine learning to pursue high-throughput screening of a large number of materials including oxides, carbonates, and sulfates, aiming to discover better radiative-cooling materials.

The work will lead to energy savings with significant promise for combating climate change. In parallel, this project will incorporate education and outreach efforts. Besides expanding the graduate and undergraduate curriculum on radiative materials, it will provide technologically attractive topics to broaden the participation from women and underrepresented groups in engineering and science.

Technical summary

The goals of this research are to develop first-principles methods for calculating thermal radiative properties, deploy an open-source code, and enable high-throughput screening of particle-matrix radiative cooling paints. Tailored thermal radiative properties are demanded in a broad set of energy and thermal-management applications. However, no open-source codes are available to predict infrared radiative properties of dielectric materials from first principles, hindering the understanding of radiative properties and the design of new radiative materials from atomic structures.

Meanwhile, although encouraging progress has been made in first-principles prediction of radiative properties, additional important phonon-scattering processes as well as phonon renormalization need to be included. Such tools will be extremely beneficial for applications such as selecting radiative-cooling materials, which are currently studied on an empirical trial-and-error basis.

In this project, the PI will address these urgent research needs via computation and data-enabled approaches. There are three specific research tasks: (1) enhancing the capabilities of first-principles prediction of thermal radiative properties beyond four-phonon scattering, by incorporating phonon renormalization, phonon-electron scattering, and phonon scattering with defects, impurities, and boundaries; (2) developing and deploying an open-source code for first-principles calculations of thermal radiative properties; and (3) coupling first-principles predictions, Monte-Carlo simulations, and machine learning to enable high-throughput screening of dielectric particle-polymer-matrix radiative-cooling paints.

The project is expected to achieve unprecedented accuracy in predicting thermal radiative properties of dielectric materials from first principles and enabling researchers to screen or design thermal radiative materials via an open-source code. It has the potential to change the current trial-and-error practice not only for radiative-cooling nanocomposites but also for many other important radiative materials such as thermal barrier coatings, thermophotovoltaic emitters, and coatings for space missions.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.