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

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.