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

CDS

• 批准号: 2102645
• 负责人: Xiulin Ruan
• 金额: $ 43万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102645&HistoricalAwards=false
• 关键词: CDS& First; Principles; Prediction; Thermal

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 summaryThermal 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 summaryThe 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.

该项目由材料研究部中的凝结和物质理论计划以及由化学，生物工程，环境和运输系统的计算和数据支持科学和工程以及热传输过程的计划进行的，NON-TECHNICAL THILON-THIMALICAL THIMALICAL THERAMALAL PLASSITION在能源和热辐射中的关键作用，包括Spraft spraft spraft sulafers and Sparececect，并在包括SPAR范围内散布辐射范围，并发挥作用。冷却。 这些应用通常需要独特的选择性辐射特性：太阳能电池需要高吸收阳光，而在大气透明窗口中，阳光的吸收低，红外光的高发射是辐射冷却的。 通过在辐射红外光到空间的同时反射阳光，已显示辐射冷却涂料可将表面冷却至低于环境温度而没有任何能量消耗。 筛选和设计此类材料要求了解热辐射特性如何取决于材料的原子结构。 但是，通常缺乏用于此目的的方法和软件工具，经验试验方法仍然是主流。 因此，该项目的目标旨在增强理论和模拟方法，这些方法可以预测其原子结构中材料的热辐射特性，并随后开发和部署开源代码，以帮助其他研究人员对自己的辐射材料进行建模。 此外，PI将使用这些工具来理解粒子 - 矩阵纳米复合材料中超有效辐射冷却的原子性起源，并采用机器学习来进行大量材料的高通量筛选，包括氧化物，碳酸盐和硫酸盐，旨在发现更好的辐射冷却材料。 这项工作将导致节省能源，并有很大的希望能够打击气候变化。 同时，该项目将纳入教育和外展工作。 除了扩大辐射材料的研究生和本科课程外，它还将提供技术吸引力的主题，以扩大妇女和代表性不足的工程和科学群体的参与。技术总结本研究的目标旨在开发第一原理的方法，用于计算开放式辐射范围，并在开放式辐射范围内进行良好的辐射范围，以启用良好的辐射范围，并启用良好的辐射范围，以启用良好的辐射范围。油漆。 在一组广泛的能量和热管理应用中，需要定制的热辐射特性。 但是，没有开源代码可以从第一原理中预测电介质材料的红外辐射特性，从而阻碍对辐射特性的理解以及原子结构的新辐射材料的设计。 同时，尽管在辐射特性的第一原理预测中已经取得了令人鼓舞的进步，但仍需要包括其他重要的声子散射过程以及声子重变。 此类工具对于选择辐射冷却材料等应用将非常有益，这些材料目前以经验试验和错误的基础进行了研究。 在该项目中，PI将通过计算和支持数据的方法来满足这些紧急研究需求。 有三个特定的研究任务：（1）通过将声子重新归一化，声子电子散射和声子散射与缺陷，杂质和边界结合，从而增强了四个子散射的热辐射特性的预测能力； （2）开发和部署一个开源代码，以用于对热辐射特性的第一原理计算； （3）将第一原理预测，蒙特卡罗模拟和机器学习耦合，以实现对介电粒子 - 聚合物 - 矩阵辐射冷却涂料的高通量筛选。 预计该项目将实现前所未有的准确性，以预测介电材料的热辐射特性，并使研究人员能够通过开源代码筛选或设计热辐射材料。 它有可能改变当前的试验练习，不仅用于放射冷却的纳米复合材料，而且还可以改变许多其他重要的辐射材料，例如热屏障涂料，热伏伏尔托马的发射器和太空任务的涂料。该奖项通过评估NSF的法定任务，并通过评估了基金会的MERIT和FORGITIAL的支持，并反映了NSF的法定任务，并已被评估。

项目成果

Thin layer lightweight and ultrawhite hexagonal boron nitride nanoporous paints for daytime radiative cooling

• DOI: 10.1016/j.xcrp.2022.101058
• 发表时间: 2022-10
• 期刊: Cell Reports Physical Science
• 影响因子: 8.9
• 作者: Andrea Felicelli;Ioanna Katsamba;Fernando Barrios;Yun Zhang;Ziqi Guo;J. Peoples;G. Chiu;X. Ruan