Award Abstract # 2119423
DMREF: Machine Learning Accelerated Design and Discovery of Rare-earth Phosphates as Next Generation Environmental Barrier Coatings

NSF Org: DMR
Division Of Materials Research
Recipient: RENSSELAER POLYTECHNIC INSTITUTE
Initial Amendment Date: August 23, 2021
Latest Amendment Date: August 23, 2021
Award Number: 2119423
Award Instrument: Standard Grant
Program Manager: Mohsen Asle Zaeem
mzaeem@nsf.gov
 (703)292-4562
DMR
 Division Of Materials Research
MPS
 Direct For Mathematical & Physical Scien
Start Date: October 1, 2021
End Date: September 30, 2025 (Estimated)
Total Intended Award Amount: $1,800,000.00
Total Awarded Amount to Date: $1,800,000.00
Funds Obligated to Date: FY 2021 = $1,800,000.00
History of Investigator:
  • Jie Lian (Principal Investigator)
    lianj@rpi.edu
  • Liping Huang (Co-Principal Investigator)
  • Suvranu De (Co-Principal Investigator)
Recipient Sponsored Research Office: Rensselaer Polytechnic Institute
110 8TH ST
TROY
NY  US  12180-3590
(518)276-6000
Sponsor Congressional District: 20
Primary Place of Performance: Rensselaer Polytechnic Institute
110 8th St
Troy
NY  US  12180-3522
Primary Place of Performance
Congressional District:
20
Unique Entity Identifier (UEI): U5WBFKEBLMX3
Parent UEI:
NSF Program(s): DMREF
Primary Program Source: 01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 024E, 054Z, 089Z, 094Z, 095Z, 8021, 8025, 8037, 8400, 9263, MANU
Program Element Code(s): 8292
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.049

ABSTRACT

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Bryce, Keith and Shih, Yueh-Ting and Huang, Liping and Lian, Jie "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" Journal of the European Ceramic Society , v.43 , 2023 https://doi.org/10.1016/j.jeurceramsoc.2023.06.030 Citation Details
Yang, Kun and Bryce, Keith and Yao, Tiankai and Zhao, Dong and Lian, Jie "Chemical durability and corrosion-induced microstructure evolution of compositionally complex titanate pyrochlore waste forms with uranium incorporation" Journal of the European Ceramic Society , 2023 https://doi.org/10.1016/j.jeurceramsoc.2023.09.071 Citation Details

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