About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
|
First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Machine Learning Guided Prediction of Thermal Properties of Rare-Earth Disilicates and Monosilicates |
Author(s) |
Mukil Ayyasamy |
On-Site Speaker (Planned) |
Mukil Ayyasamy |
Abstract Scope |
Rare-earth silicates are promising candidate materials for the application of environmental barrier coatings (EBC) for SiC composites in gas-turbine engines. One of the most important design criteria is the good coefficient of thermal expansion (CTE) match with that of the SiC composites. Although theoretical rules and computational methods are of practical use for CTE prediction, unsatisfactory predictability and universality of models across different materials of the class impede rational design of EBC materials. In this work, we build a machine learning model for CTE prediction that is universal to both rare-earth disilicates and monosilicates. We consider descriptors based on unit cell polyhedral features obtained from our density functional theory calculations. We employ support vector regression ensembles via bootstrapping to determine prediction uncertainty along with the CTE predictions. Additionally, our model interpretability methods reveal quantitative structure-property relationships. |
Proceedings Inclusion? |
Undecided |