About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Automated Classification of Powder X-ray Diffraction Data Using Deep Learning |
Author(s) |
Jerardo Salgado, Zhaotong Du, Samuel Lerman, Chenliang Xu, Niaz Abdolrahim |
On-Site Speaker (Planned) |
Jerardo Salgado |
Abstract Scope |
Emerging X-ray scattering techniques provide the capability to probe the correlations between structure and material properties with sensitivities approaching the single-molecule level and picosecond resolution. However, temporal diffraction images at extreme temperatures and pressures generate terabytes of data, making it impossible for experts to analyze. With the increased demand for new insights at these conditions and big data process capabilities, our goal is to apply artificial intelligence to characterize inorganic materials accurately. We trained deep learning models with simulated Xrds – synthetic profiles from existing verified materials - then used the models to make predictions on experimental data. Our convolutional neural network and dense neural network models have shown state-of-the-art performance on synthetic data, achieving testing accuracies of 97% for crystal structure classification. The model’s ability to adapt to experimental domains was also studied, where accuracies have reached 70% and 55% for crystal system and space group classification respectively. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Computational Materials Science & Engineering, Powder Materials |