A materials informatics approach for composition and property prediction of polymer-derived silicon oxycarbides

Abstract

Polymer-derived silicon oxycarbide (SiOC) materials enable the formation of homogeneous microstructures and high temperature stable properties. However, the relationships between the processing parameters and microstructures/properties have not been clearly understood. In this study, a materials informatics approach was employed to the SiOC materials to analyze and estimate the relationships. Datasets were constructed from results of previously reported literature about SiOC. The correlation analysis provided processing parameter ranking regarding the corresponding influences on the properties and microstructures. Such an understanding can be utilized for desired material fabrication. Machine learning models with high accuracy were proposed using the ranked features obtained from the correlation analysis. In addition, important points on the data collection, correlation analysis, and machine learning as well as limitations of the current dataset were discussed. The proposed workflow for the SiOC materials can be extended to different types of polymer-derived ceramics by incorporating various features and targets involved in the processing variables, microstructures, and properties.