DMREF: Collaborative Research: GOALI: Accelerating Discovery of High Entropy Silicates for Extreme Environments

非技术描述：涡轮发动机用于动力和推进的效率可以通过在更高的温度下运行来提高。然而，这种方法受到能够承受这些极端环境的可用材料的限制。在这个设计材料革命和设计我们的未来(DMREF)项目中，通过用实验结果验证的计算方法，将加速发现能够实现更高温度涡轮机运行的新材料。将研究的材料包括用于涡轮发动机部件潜在高温涂层的混合稀土硅酸盐。目前正在开发的涂料在硅酸盐中使用单一的稀土元素。在硅酸盐中混合15种稀土元素的各种组合提供了发现和优化所需涂层性能的机会，包括在反应性涡轮发动机环境中的低导热系数和高稳定性。高通量的计算方法将被用来了解材料性能的趋势，因为组成是不同的。将向参与该项目的大学生传授加速材料发现的概念，并通过外展活动向小学生展示材料在发动机中的应用和重要性。技术描述：本研究将促进对极端环境下阳离子复杂性对高熵稀土硅酸盐相稳定性影响的新理解。计算-实验-反馈回路与机器学习和高通量计算相结合，将导致迄今尚未实现的熵诱导材料稳定性、热物性和耐腐蚀性之间的联系。该项目将在对可为特定极端环境应用设计的新材料的基本理解和发现方面取得进展。材料发现的计算方法将利用FLOW：高通量性质预测。这些预测将通过对固态烧结合成的稀土硅酸盐、改进阳离子混合的化学技术以及气相脉冲激光沉积薄膜的表征来验证。相稳定性和化学无序性将通过包括X射线衍射和透射电子显微镜在内的技术来表征。由此产生的稀土硅酸盐混合物的稳定性将有助于改进材料发现的计算方法。此外，还将使用计算方法来预测声子传输和热性质。通过使用时间域和稳态热反射以及热盘技术，将这些预测的热物性与作为温度函数的热导率测量进行比较。环境稳定性将使用“蒸汽喷射”测试进行实验表征，这是一种极端环境实验室测试，可产生代表燃烧环境的高温、高速、反应性蒸汽。热和环境测试的结果将用于验证和推进计算方法和基于属性的材料发现。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Non-technical Description: The efficiency of turbine engines used for power and propulsion can be increased by operating at higher temperatures. However, this approach is limited by available materials that can withstand these extreme environments. In this Designing Materials to Revolutionize and Engineer our Future (DMREF) project, the discovery of new materials that enable higher temperature turbine operation will be accelerated through computational methods that are validated with experimental results. Materials to be studied include mixed rare earth silicates for potential high temperature coatings of turbine engine components. Coatings currently under development use a single rare earth element in the silicate. Mixing various combinations of the fifteen rare earth elements in the silicates provides opportunities to discover and optimize desirable coating properties, including low thermal conductivity and high stability in the reactive turbine engine environments. High throughput computational approaches will be used to understand trends in material properties as the composition is varied. The concept of accelerated material discovery will be taught to the university students involved in the project and the application and importance of materials in engines will be demonstrated to elementary students through outreach activities.Technical Description: This research will accelerate new understanding of the interplay of cation complexity on phase stability of high entropy rare earth silicates in extreme environments. The computation-experiment-feedback loop coupled with machine learning and high throughput computation will result in heretofore unrealized linkages of entropy-induced material stability, thermal properties, and corrosion resistance. The project will result in advances in fundamental understanding and discovery of novel materials that can be designed for specific extreme environment applications. The computational approach to materials discovery will utilize AFLOW: high throughput property prediction. These predictions will be tested by characterizing rare earth silicates synthesized via solid state sintering, chemical techniques for improved cation mixing, and gas phase pulsed laser deposition of thin films. Phase stability and chemical disorder will be characterized through use of techniques including X-ray diffraction and transmission electron microscopy. Resulting stability of rare earth silicate mixtures will inform improvements in the computational approach for materials discovery. Additionally, computational approaches will be used to predict phonon transport and thermal properties. These predicted thermal properties will be compared against thermal conductivity measurements as a function of temperature through use of time domain and steady state thermoreflectance, and hot disk techniques. Environmental stability will be experimentally characterized using "steam-jet" testing, an extreme environment laboratory test creating high-temperature, high-velocity, reactive steam representative of the combustion environment. Results from both the thermal and environmental testing will be used to validate and advance the computational approaches and property-based materials discovery.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.