|About this Abstract
|2022 TMS Annual Meeting & Exhibition
|AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|M-11: Machine Learning Based Prediction of Melting Temperature and Coefficient of Thermal Expansion Coefficient Using SciGlass Datasets
|Jong Ho Kim
|On-Site Speaker (Planned)
|Jong Ho Kim
Ceramic materials vary in properties depending on composition and can be designed with various materials depending on composition. Existing composition design was a way to explore desired composition based on domain knowledge, but a new method has recently been proposed due to rapid advances in machine learning and artificial intelligence.
An open database SciGlass exists for the composition of glass materials and provides material properties in various compositions. It provides more than 30,000 data sets of materials, enabling machine learning using them. In this work, we test a model to measure melting temperature and thermal expansion coefficients of glass or ceramic materials using various machine learning techniques. As a result, both traditional methods such as random forest and recent deep learning models have shown satisfactory results. The results of the Factsage calculation were compared with that of the two-component ceramic material and showed some consistent results.
|Computational Materials Science & Engineering, Ceramics, ICME