{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-classification-of-small-x-ray-1","title":"Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks","arxiv_id":null,"date":"2019-05-17","proceeding":"npj Computational Materials 2019 5","authors":["Felipe Oviedo","Zekun Ren","Shijing Sun","Charles Settens","Zhe Liu","Noor Titan Putri Hartono","Savitha Ramasamy","Brian L. DeCost","Siyu I. P. Tian","Giuseppe Romano","Aaron Gilad Kusne","Tonio Buonassisi"],"abstract":"X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal-halides spanning three dimensionalities and seven space groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross-validated accuracies for dimensionality and space group classification of 93 and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16° 2θ, which enables an XRD pattern to be obtained and classified in 5.5 min or less.","url_abs":"https://www.nature.com/articles/s41524-019-0196-x","url_pdf":"https://www.nature.com/articles/s41524-019-0196-x.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fast-classification-of-small-x-ray-1","repo_url":"https://github.com/PV-Lab/AUTO-XRD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"fast-classification-of-small-x-ray-1","repo_url":"https://github.com/PV-Lab/autoXRD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"interpretable-machine-learning","task_name":"Interpretable Machine Learning"},{"task_slug":"material-classification","task_name":"Material Classification"},{"task_slug":"material-recognition","task_name":"Material Recognition"},{"task_slug":"space-group-classification","task_name":"Space group classification"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"},{"task_slug":"x-ray-diffraction-xrd","task_name":"X-Ray Diffraction (XRD)"}],"methods":[{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}