|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Direct Prediction of Mechanical Properties from X-ray Diffraction Patterns Using Machine Learning
||Naoki Hato, Masaya Kumagai, Ken Kurosaki
|On-Site Speaker (Planned)
The development of new materials requires many steps, such as sample preparation, X-ray diffraction measurement and analysis, property measurement, and performance evaluation, and requires an enormous amount of time. In recent years, high-throughput material synthesis and high-speed X-ray diffraction measurements have made sample preparation and X-ray diffraction measurements more efficient. However, the process from crystal structure analysis to performance evaluation has not yet become efficient. In this study, we attempted to predict mechanical properties directly from X-ray diffraction patterns using machine learning. Specifically, we used an optimal feature vector based on the X-ray diffraction pattern to predict the crystal structure and mechanical properties that are highly related to the crystal structure.
||Machine Learning, Mechanical Properties,