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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
Presentation Title Fast Crystal Structure Reconstruction and Prediction Method: Based on X-ray Diffraction Dataset and Neural Network
Author(s) Cheng-Che Tung, Yan-Zhen Chen, Yuan-Yu Lin, Nan-Yow Chen, An-Cheng Yang, Po-Yu Chen
On-Site Speaker (Planned) Cheng-Che Tung
Abstract Scope The existing X-ray diffraction (XRD) data acquisition and analysis process is time-consuming and requires the assistance of a database. In this study, we propose a machine learning based workflow which can directly perform crystal system classification and structure regression from XRD patterns. We obtained the theoretically calculated structure and XRD patterns of 123,904 crystals through Materials Project's API. The XRD patterns were used as a dataset and trained the neural network by supervised learning. We achieved more than 92% validation accuracy in classification, while mean-square error can be lower than 0.15 in regression. The 3D structure of the crystal can be predicted and reconstructed rapidly, even if the composition is unknown, after input the XRD data. This study provides a framework for effectively using massive data of material and has the potential to be extended to many microstructure forecasting.
Proceedings Inclusion? Planned:

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