DMREF: Machine Learning Accelerated Design and Discovery of Rare-earth Phosphates as Next Generation Environmental Barrier Coatings

DMREF：机器学习加速设计和发现稀土磷酸盐作为下一代环境屏障涂层

• 批准号: 2119423
• 负责人: Jie Lian
• 金额: $ 180万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2119423&HistoricalAwards=false
• 关键词: DMREF; Machine; Learning; Accelerated; Design

Environmental barrier coatings (EBCs) are key components that can greatly enhance the performance/longevity of structural materials such as ceramic-matrix composites against active oxidation in high speed hot gas streams and corrosion in reactive engine environments. Multi-generation EBCs have evolved, mainly based on silicate-based systems, but they suffer from the volatility of silicon due to water vapor attack and corrosion of molten glass attack. Innovative design and discovery of EBCs with transformative performance are needed to meet even harsher environments of high temperature, high thermal flux and severe oxidation and corrosion for future aerospace and space systems. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project will explore an innovative concept of using multiple component rare-earth phosphates as advanced EBCs, and develop a science-based paradigm guided by machine learning (ML) for accelerated materials design and discovery. Both graduate and undergraduate students will be trained as the next-generation workforce in this data-driven materials research. K-12 students and underrepresented groups will be engaged through multiple outreach activities such as the Engineering Summer Exploration program at Rensselaer and the New Visions: Math, Engineering, Technology & Science program. Materials data and computational tools developed will be contributed to the MPContribs Portal for public access on the Materials Project platform to facilitate data-driven material design. Material design and discovery for advanced environmental barrier coatings (EBCs) have been greatly hindered by our limited understanding of how composition and microstructure affect materials properties and performance. This project will accelerate fundamental understanding of the influence of composition and microstructure on the phase stability and properties of multicomponent rare-earth phosphates, and use this understanding to optimize performance of next generation EBCs for ceramic matrix composites (CMCs) in reactive engine environments. A multipronged data-driven machine learning (ML) approach will be developed to inform materials design and guide materials performance evaluation to discover new rare-earth phosphates that have unique attributes of EBCs for CMCs, compared to current state-of-the-art disilicates without the issue of silicon evaporation. An element-based ML will be trained on high throughput density functional theory calculations and will be used to guide the design and optimization of configurationally-disordered rare-earth phosphates with key characteristics of EBCs. A microstructure-based ML will be trained on high-throughput finite element method calculations and will be used to predict the optimal microstructure and performance of EBCs against molten glass corrosion at elevated temperatures. The integration of multiscale computations, machine learning, and experimental demonstration and validation will provide a pathway for success in accelerating the design and discovery of rare-earth phosphates as next generation EBCs for CMCs.This project is jointly funded by NSF’s Mathematical and Physical Sciences (MPS) Division of Materials Research (DMR) Designing Materials to Revolutionize and Engineer our Future (DMREF) program, and the Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) in the Directorate for Engineering (ENG).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.

环境屏障涂层（EBC）是可以大大提高结构材料（例如陶瓷基复合材料）在高速热气流中的活性氧化和在反应性发动机环境中的腐蚀的性能/寿命的关键部件。多代EBC已经发展，主要基于硅酸盐基系统，但是它们遭受由于水蒸气侵蚀和熔融玻璃侵蚀而导致的硅的挥发性。需要创新设计和发现具有变革性性能的EBC，以满足未来航空航天和空间系统的高温，高热通量和严重氧化和腐蚀等更恶劣的环境。这个设计材料以革命和工程我们的未来（DMREF）项目将探索使用多组分稀土磷酸盐作为先进EBC的创新概念，并开发一种以机器学习（ML）为指导的科学范式，以加速材料设计和发现。研究生和本科生都将接受培训，成为这种数据驱动的材料研究的下一代劳动力。K-12学生和代表性不足的群体将通过多种外展活动，如工程夏季探索计划在伦斯勒和新视野：数学，工程，技术科学计划。开发的材料数据和计算工具将贡献给MPContribs门户网站，供公众在材料项目平台上访问，以促进数据驱动的材料设计。由于我们对组成和微观结构如何影响材料性能的理解有限，先进环境屏障涂层（EBC）的材料设计和发现受到了很大的阻碍。该项目将加速对组成和微观结构对多组分稀土磷酸盐的相稳定性和性能的影响的基本理解，并利用这种理解来优化反应发动机环境中陶瓷基复合材料（CMC）的下一代EBC的性能。将开发一种多管齐下的数据驱动的机器学习（ML）方法，为材料设计提供信息，并指导材料性能评估，以发现与目前最先进的二硅酸盐相比，具有CMC EBC独特属性的新型稀土磷酸盐，而不会出现硅蒸发问题。基于元素的ML将接受高通量密度泛函理论计算的培训，并将用于指导具有EBC关键特征的构型无序稀土磷酸盐的设计和优化。基于微观结构的ML将接受高通量有限元方法计算的培训，并将用于预测EBC在高温下抵抗熔融玻璃腐蚀的最佳微观结构和性能。多尺度计算、机器学习、实验演示和验证的集成将为加速设计和发现稀土磷酸盐作为CMC的下一代EBC提供成功的途径。该项目由NSF的数学和物理科学（MPS）材料研究部（DMR）设计材料以革命和工程我们的未来（DMREF）计划共同资助，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Calcium-Magnesium-Aluminosilicate (CMAS) corrosion resistance of high entropy rare-earth phosphate (Lu0.2Yb0.2Er0.2Y0.2Gd0.2)PO4: A novel environmental barrier coating candidate

• DOI: 10.1016/j.jeurceramsoc.2023.06.030
• 发表时间: 2023-06
• 期刊: Journal of the European Ceramic Society
• 影响因子: 5.7
• 作者: Keith Bryce;Yueh-Ting Shih;Liping Huang;Jie Lian

Chemical durability and corrosion-induced microstructure evolution of compositionally complex titanate pyrochlore waste forms with uranium incorporation

• DOI: 10.1016/j.jeurceramsoc.2023.09.071
• 发表时间: 2023-09
• 期刊: Journal of the European Ceramic Society
• 影响因子: 5.7
• 作者: Kun Yang;Keith Bryce;Tiankai Yao;Dong Zhao;Jie Lian