| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
|
| Presentation Title |
Machine Learning Approach for On-the-fly Crystal System Classification from Powder X-ray Diffraction Pattern |
| Author(s) |
Yuta Suzuki, Hideitsu Hino, Takafumi Hawai, Kotaro Saito, Kanta Ono |
| On-Site Speaker (Planned) |
Yuta Suzuki |
| Abstract Scope |
The Crystal system and space group determination in the initial stage of crystal structure analysis is one of the time-consuming processes in materials research. We demonstrate an automated method to predict crystal systems and space groups from X-ray diffraction (XRD) patterns using a machine learning (ML) technique. The XRD dataset was calculated from crystal structures in ICSD (Inorganic Crystal Structure Database) with pymatgen middleware. Our tree-based ML model marks over 90% accuracy for crystal system prediction and 88% for space group prediction with five candidates. We applied this method to an actual XRD experiment for VO2 and confirmed that our method works for actual experimental data. By using an interpretable machine-learning approach, we also succeeded in quantifying empirical knowledge of experts. Our result shows the possibility of the data-driven discovery of unrecognized characteristics embedded in experimental data and will contribute to the realization of on-the-fly data analysis. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |