Details

Autor(en) / Beteiligte

• Ayyasamy, Mukil V.
• Deijkers, Jeroen A.
• Wadley, Haydn N.G.
• Balachandran, Prasanna V.

Titel

Density functional theory and machine learning guided search for RE2Si2O7 with targeted coefficient of thermal expansion

Ist Teil von

• Journal of the American Ceramic Society, 2020-08, Vol.103 (8), p.4489-4497

Ort / Verlag

Columbus: Wiley Subscription Services, Inc

Erscheinungsjahr

2020

Quelle

Wiley Online Library Journals Frontfile Complete

Beschreibungen/Notizen

• Density functional theory (DFT) calculations and machine learning (ML) methods are used to establish a relationship between the crystal structures of rare‐earth (RE) disilicates (RE2Si2O7) and their coefficient of thermal expansion (CTE). The DFT total energy data predict the presence of several energetically competing crystal structures, which is rationalized as one of the reasons for observing polymorphism. An ensemble of support vector regression models is trained to rapidly predict the CTE as a function of RE2Si2O7 crystal chemistry. Experiments subsequently validated the structure and CTE predictions for Sm2Si2O7. Apparent Bulk Coefficients of Thermal Expansion (ABCTE) of various RE2Si2O7 compounds in different polymorphs. The contribution of this work is to first predict and then experimental validate the ABCTE for Sm2Si2O7 compound in the P41 space group, which adds to the knowledge‐base of environmental barrier coating materials.