HTEM AI-XRD: Large-scale deployment of AI interpretation of X-Ray Diffraction Spectra
Authors: 
Hilary Egan, Max C. Gallant, Davi Febba Marcelo, Nick Wunder, Joseph Smith, Andriy ZakutayevAuthors Info & Claims
Hilary Egan, Max C. Gallant, Davi Febba Marcelo, Nick Wunder, Joseph Smith, Andriy ZakutayevAuthors Info & ClaimsArticle No.: 39, Pages 1 - 5
Abstract
Experimentally synthesizing predicted materials in a reproducible manner remains a key bottleneck in materials science progress. Autonomous synthesis and closed loop integration of prediction and characterization can address these issues, however, this requires autonomous characterization methods for all analysis including crystallographic phase identification which currently remains a rate-limiting step. Here we demonstrate initial results in applying a collection of Artificial Intelligence (AI)-based X-Ray Diffraction (XRD) analysis methods at scale, using a workflow running across various compute platforms from High Performance Computing (HPC) to off-premise cloud. We detail the workflow methods, compare model performance on synthetic and experimental data across a wide-array of material systems, and analyze the performance of the AI methods in comparison to manually labeled experimental data.
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- HTEM AI-XRD: Large-scale deployment of AI interpretation of X-Ray Diffraction Spectra
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Published In
July 2024
608 pages
ISBN:9798400704192
DOI:10.1145/3626203
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
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Published: 17 July 2024
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PEARC '24: Practice and Experience in Advanced Research Computing
July 21 - 25, 2024
RI, Providence, USA
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