A Machine Learning-Derived Atomistic Potential for Y2Si2O7

Abstract

Incorporation of SiC/SiC ceramic matrix composite (CMC) hot section components into aircraft engines promises to increase efficiency and safety. However, SiC/SiC CMCs are subject to water vapor-induced oxidation and recession at the high temperatures of engine operation, and thus environmental barrier coatings (EBCs) are required to reduce this degradation and enable their widespread adoption. An understanding of EBCs failure mechanisms, including thermochemical and thermomechanical mechanisms, is essential as coating degradation leads to reduced CMC component service life. Computational modeling approaches can provide insight into EBC material properties important for coating design. However, density functional theory (DFT) is computationally expensive and atomistic potentials are lacking for materials of interest. In this work, we utilize a machine learning approach and DFT training data to parameterize atomistic potentials for two candidate EBC materials, Y2Si2O7 and Yb2Si2O7. These potentials enable near DFT-accurate calculations of thermodynamic and thermomechanical properties essential to EBC design.