| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
|
| Presentation Title |
Composition and Property Prediction of Polymer-derived Silicon Oxycarbides |
| Author(s) |
Yi Je Cho, Harrison Chaney, Kathy Lu |
| On-Site Speaker (Planned) |
Yi Je Cho |
| Abstract Scope |
Polymer-derived silicon oxycarbide (SiOC) materials enable the formation of homogeneous microstructures and high temperature stable properties. However, the relationship between the processing parameters and microstructures/properties has not been clearly understood. In this study, a materials informatics approach was employed to analyze and estimate this relationship. The correlation analysis provided importance ranking of the process parameters, which can be later utilized for the fabrication process. Machine learning models with high accuracy were proposed using the ranked features obtained from the correlation analysis. ReaxFF simulations were performed to evaluate the fidelity of the machine learning models and to confirm the feasibility of the models for designing new SiOC materials. The data analytics workflow proposed in this study can be extended to different types of polymer-derived ceramics by incorporating various features and targets involved in the process, microstructures, and properties. |