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Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, the development of new coating systems is hindered by the large design space and difficulty in predicting the properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y2Si2O7, which is then applied to calculate the thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict the phonon-based thermodynamic properties of Y2Si2O7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y2Si2O7. Molecular dynamics trajectories using the DNP correctly demonstrate the accurate prediction of the anisotropy of the CTE in good agreement with the diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y2Si2O7 compared to DFT or experiments.
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This content will become publicly available on October 1, 2026
Thermomechanical properties of rare earth phosphates as environmental barrier coatings
Abstract This study integrated high‐throughput computational modeling with experimental validation to investigate rare earth (RE) phosphates as potential environmental barrier coatings (EBCs) for SiC‐based ceramic matrix composites (CMCs). Although RE silicates have been widely studied for EBC applications, they are prone to degradation due to water vapor corrosion and silica volatilization at high temperatures. RE phosphates, with their strong P–O bonds, offer a promising alternative with improved resistance to volatilization. Using the AFLOW computational framework, we performed density functional theory calculations to evaluate the thermomechanical properties of single‐component RE phosphates. Specifically, AFLOW Automatic Elasticity Library (AEL) was employed to predict mechanical properties, and AFLOW Automatic GIBBS Library (AGL) and AFLOW Quasiharmonic Approximation (QHA) were used to estimate thermal properties. Our results indicate that although the AGL method performs well in predicting thermal conductivity, it may not be suitable for screening the coefficient of thermal expansion of RE phosphates. Additionally, we explored the concept of configurational disorder in high‐entropy phosphates to enhance their thermal performance. Our experimental validation supported the computational findings, demonstrating that incorporating multiple RE elements into phosphates can significantly improve the performance of EBCs for SiC‐based CMCs.
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- Award ID(s):
- 2119423
- PAR ID:
- 10639494
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Journal of the American Ceramic Society
- Volume:
- 108
- Issue:
- 10
- ISSN:
- 0002-7820
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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