{"id":588406,"date":"2023-12-19T08:27:12","date_gmt":"2023-12-19T13:27:12","guid":{"rendered":"https:\/\/www.rochester.edu\/newscenter\/?p=588406"},"modified":"2023-12-20T14:44:45","modified_gmt":"2023-12-20T19:44:45","slug":"machine-learning-x-ray-diffraction-new-materials-576352","status":"publish","type":"post","link":"https:\/\/www.rochester.edu\/newscenter\/machine-learning-x-ray-diffraction-new-materials-576352\/","title":{"rendered":"Machine learning boosts search for new materials"},"content":{"rendered":"<h2>Scientists have developed deep-learning models that can sift through the massive amounts of data generated by X-ray diffraction techniques.<\/h2>\n<p>Scientists from the <a href=\"https:\/\/rochester.edu\/\">University of Rochester<\/a> say deep learning can supercharge a technique that is already the gold standard for characterizing new materials. In an <a href=\"https:\/\/doi.org\/10.1038\/s41524-023-01164-8\"><em>npj Computational Materials<\/em> paper<\/a>, the interdisciplinary team describes models they developed to better leverage the massive amounts of data that X-ray diffraction experiments produce.<\/p>\n<p>During X-ray diffraction experiments, bright lasers shine on a sample, producing diffracted images that contain important information about the material\u2019s structure and properties. Project lead <a href=\"https:\/\/www.hajim.rochester.edu\/me\/people\/faculty\/abdolrahim_niaz\/index.html\">Niaz Abdolrahim<\/a>, an associate professor in the <a href=\"https:\/\/www.hajim.rochester.edu\/me\/index.html\">Department of Mechanical Engineering<\/a> and a scientist at the <a href=\"https:\/\/www.lle.rochester.edu\/\">Laboratory for Laser Energetics (LLE)<\/a>, says conventional methods of analyzing these images can be contentious, time-consuming, and often ineffective.<\/p>\n<p>\u201cThere is a lot of materials science and physics hidden in each one of these images and terabytes of data are being produced every day at facilities and labs worldwide,\u201d says Abdolrahim. \u201cDeveloping a good model to analyze this data can really help expedite materials innovation, understand materials at extreme conditions, and develop materials for different technological applications.\u201d<\/p>\n<p>The study, led by <a href=\"https:\/\/www.hajim.rochester.edu\/matsci\/graduate\/phd-requirements.html\">materials science PhD<\/a> student Jerardo Salgado, holds particular promise for high-energy-density experiments like those conducted at LLE by researchers from the <a href=\"https:\/\/cmap.rochester.edu\/\">Center for Matter at Atomic Pressures<\/a>. By examining the precise moment when materials under extreme conditions change phases, scientists can discover ways to create new materials and learn about the formation of stars and planets.<\/p>\n<p>Abdolrahim says the project, <a href=\"https:\/\/www.rochester.edu\/newscenter\/machine-learning-pinpoints-when-matter-changes-under-extreme-conditions-527492\/\">funded by the US Department of Energy\u2019s National Nuclear Security Administration and the National Science Foundation<\/a>, improves upon previous attempts to develop machine learning models for X-ray diffraction analysis that were trained and evaluated primarily with synthetic data. Abdolrahim, Associate Professor <a href=\"https:\/\/www.cs.rochester.edu\/people\/faculty\/xu_chenliang\/index.html\">Chenliang Xu<\/a> from the <a href=\"https:\/\/www.cs.rochester.edu\/index.html\">Department of Computer Science<\/a>, and their students incorporated real-world data from experiments with inorganic materials to train their deep-learning models.<\/p>\n<p>More X-ray diffraction analysis experimental data needs to be publicly available to help refine the models, according to Abdolrahim. She says the team is working on creating platforms for others to share data that can help train and evaluate the system, making it even more effective.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Rochester scientists have developed deep-learning models that can sift through the massive amounts of data generated by X-ray diffraction techniques.<\/p>\n","protected":false},"author":1242,"featured_media":588416,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[116],"tags":[18802,23312,18632,5296,37312,18572],"class_list":["post-588406","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sci-tech","tag-department-of-computer-science","tag-department-of-mechanical-engineering","tag-hajim-school-of-engineering-and-applied-sciences","tag-laboratory-for-laser-energetics","tag-materials-science-program","tag-research-finding"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Machine learning boosts search for new materials<\/title>\n<meta name=\"description\" content=\"Rochester scientists have developed deep-learning models that can sift through the massive amounts of data generated by X-ray diffraction techniques.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.rochester.edu\/newscenter\/machine-learning-x-ray-diffraction-new-materials-576352\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine learning boosts search for new materials\" \/>\n<meta property=\"og:description\" content=\"Rochester scientists have developed deep-learning models that can sift through the massive amounts of data generated by X-ray diffraction techniques.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.rochester.edu\/newscenter\/machine-learning-x-ray-diffraction-new-materials-576352\/\" \/>\n<meta property=\"og:site_name\" content=\"News Center\" \/>\n<meta property=\"article:published_time\" content=\"2023-12-19T13:27:12+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-12-20T19:44:45+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.rochester.edu\/newscenter\/wp-content\/uploads\/2023\/12\/fea-x-ray-diffraction-image-plate-1200x630.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"630\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Luke Auburn\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Luke Auburn\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/machine-learning-x-ray-diffraction-new-materials-576352\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/machine-learning-x-ray-diffraction-new-materials-576352\\\/\"},\"author\":{\"name\":\"Luke Auburn\",\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/#\\\/schema\\\/person\\\/e928dc2863b53a89ece6d40c7992a4e1\"},\"headline\":\"Machine learning boosts search for new materials\",\"datePublished\":\"2023-12-19T13:27:12+00:00\",\"dateModified\":\"2023-12-20T19:44:45+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/machine-learning-x-ray-diffraction-new-materials-576352\\\/\"},\"wordCount\":370,\"image\":{\"@id\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/machine-learning-x-ray-diffraction-new-materials-576352\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.rochester.edu\\\/newscenter\\\/wp-content\\\/uploads\\\/2023\\\/12\\\/fea-x-ray-diffraction-image-plate.jpg\",\"keywords\":[\"Department of Computer Science\",\"Department of Mechanical Engineering\",\"Hajim School of Engineering and Applied Sciences\",\"Laboratory for Laser Energetics\",\"Materials Science Program\",\"research finding\"],\"articleSection\":[\"Science &amp; 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