{"id":364135,"date":"2026-03-02T16:16:14","date_gmt":"2026-03-02T16:16:14","guid":{"rendered":"https:\/\/www.europesays.com\/ie\/364135\/"},"modified":"2026-03-02T16:16:14","modified_gmt":"2026-03-02T16:16:14","slug":"artificial-intelligence-makes-x-ray-spectroscopy-five-times-faster-smarter-and-less-prone-to-human-error","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ie\/364135\/","title":{"rendered":"Artificial intelligence makes X\u2011ray spectroscopy five times faster, smarter and less prone to human error"},"content":{"rendered":"

Artificial intelligence<\/a>\u00a0(AI<\/a>) is transforming nearly every branch of science. And researchers at the U.S. Department of Energy\u2019s (DOE) Argonne National Laboratory are helping lead the way.<\/p>\n

\u201cThere is a lot of hype around\u00a0AI\u00a0today in the media,\u201d said Mathew Cherukara, a computational scientist and group leader at Argonne\u2019s Advanced Photon Source (APS),<\/strong>\u00a0a\u00a0DOE\u00a0Office of Science user facility.\u00a0\u200b\u201cYet there is no question that\u00a0AI\u00a0can help researchers at\u00a0APS\u00a0and other light sources make breakthroughs in advanced chemical processes critical to American industry.\u201d<\/p>\n

As proof, the Argonne team has developed an AI-guided method that dramatically speeds up a widely used\u00a0X-ray<\/a>\u00a0technique known as X-ray absorption near-edge structure (XANES) spectroscopy. It does so with far less risk of human error or damage to the sample from the X-ray beams.<\/p>\n

This powerful analytical tool reveals the hidden chemistry inside materials important to modern life, such as batteries<\/a>, catalysts<\/a> and materials through which electricity flows without resistance. The team\u2019s\u00a0AI\u00a0approach cuts the number of measurements previously needed by as much as 80%, with no loss of accuracy. The result is a dramatic shortening of data acquisition duration, allowing researchers to capture fast chemical changes in real time.<\/p>\n

Here\u2019s how\u00a0XANES\u00a0works: Scientists shine X-ray beams with increasing energy onto a material. Each X-ray beam is a tiny packet of energy. When the energy is high enough to knock a tightly bound electron out of an atom, the material suddenly absorbs more X-rays. This sharp jump in absorption is called the absorption edge.<\/p>\n

By tracking how X-ray absorption changes before, during and after this edge, researchers can watch the chemistry of a specific element unfold within a material, from how a metallic catalyst reacts with other chemicals to how the charge state of a battery element changes during cycling.<\/p>\n

\u201cXANES\u00a0is incredibly powerful, but until now, scientists had to make dozens or even hundreds of choices about where to measure and how long to measure at each X-ray energy level,\u201d said Shelly Kelly, an\u00a0APS\u00a0physicist and group leader.<\/p>\n

Some regions of X-ray energy are rich with chemical information, calling for numerous measurements. Other parts are not, meaning far fewer measurements are needed.\u00a0\u200b\u201cIt is often not easy for experimenters to set the optimal number of measurements to make in a given energy region,\u201d Kelly said.\u00a0\u200b\u201cAI\u00a0is helping us take the guesswork out of\u00a0XANES.\u201d<\/p>\n

The team\u2019s new approach replaces the manual measurement process with an\u00a0AI\u00a0algorithm that automatically selects the most useful measurement points. The algorithm identifies where the absorption edge is likely to occur, which regions hold the most chemical detail and which regions offer little added information.<\/p>\n

\u201cOur\u00a0AI\u00a0method measures only where needed,\u201d said Ming Du, a computational scientist and lead author on the paper.\u00a0\u200b\u201cIt\u2019s smarter, faster and more efficient, and it lets researchers focus on the big picture.\u201d<\/p>\n

The system also enables something new: AI-directed experiments. By comparing a sample\u2019s evolving spectrum against known starting and ending states (for example, a fully charged electrode versus fully discharged), the\u00a0AI\u00a0can tell researchers in real time the state of the chemical progress, when enough information has been collected, and when it\u2019s time to move on.<\/p>\n

\u201cIt\u2019s not just speeding up the measurement,\u201d Kelly said.\u00a0\u200b\u201cIt\u2019s making decisions during the experiment \u2014 decisions a human used to make.\u201d<\/p>\n

The work points toward a future in which X-ray beamlines, such as those at the\u00a0APS, are more autonomous and better able to track complex chemical reactions as they happen.<\/p>\n

\u201cThis brings us closer to intelligent X-ray stations that make the most of every photon,\u201d Cherukara said.\u00a0\u200b\u201cArgonne plans to continue developing AI-driven tools for next-generation X-ray science, especially as the upgraded\u00a0APS\u00a0delivers beams up to 500 times brighter than before.\u201d<\/p>\n

The team demonstrated the method using beamlines\u00a025-ID-C,\u00a020-BM\u00a0and\u00a010-ID\u00a0at the\u00a0APS. The project was supported by the\u00a0DOE\u00a0Office of Science, Office of Basic Energy Sciences.<\/p>\n

The research first appeared in npj Computational Materials. In addition to Du, Kelly and Cherukara, contributors include Mark Wolfman and Chengjun Sun.<\/p>\n

About the\u00a0Advanced Photon Source<\/strong><\/p>\n

The U. S. Department of Energy Office of Science\u2019s Advanced Photon Source (APS) at Argonne National Laboratory is one of the world\u2019s most productive X-ray light source facilities. The\u00a0APS\u00a0provides high-brightness X-ray beams to a diverse community of researchers in materials science, chemistry, condensed matter physics, the life and environmental sciences, and applied research. These X-rays are ideally suited for explorations of materials and biological structures; elemental distribution; chemical, magnetic, electronic states; and a wide range of technologically important engineering systems from batteries to fuel injector sprays, all of which are the foundations of our nation\u2019s economic, technological, and physical well-being. Each year, more than 5,000 researchers use the\u00a0APS\u00a0to produce over 2,000 publications detailing impactful discoveries, and solve more vital biological protein structures than users of any other X-ray light source research facility.\u00a0APS\u00a0scientists and engineers innovate technology that is at the heart of advancing accelerator and light-source operations. This includes the insertion devices that produce extreme-brightness X-rays prized by researchers, lenses that focus the X-rays down to a few nanometers, instrumentation that maximizes the way the X-rays interact with samples being studied, and software that gathers and manages the massive quantity of data resulting from discovery research at the\u00a0APS.<\/p>\n

This research used resources of the Advanced Photon Source, a U.S.\u00a0DOE\u00a0Office of Science User Facility operated for the\u00a0DOE\u00a0Office of Science by Argonne National Laboratory under Contract No.\u00a0DE-AC02-06CH11357.<\/p>\n

Argonne National Laboratory<\/a><\/strong>\u00a0seeks solutions to pressing national problems in science and technology by conducting leading-edge basic and applied research in virtually every scientific discipline. Argonne is managed by\u00a0UChicago Argonne,\u00a0LLC<\/a>\u00a0for the\u00a0U.S. Department of Energy\u2019s Office of Science.<\/a><\/p>\n

The U.S. Department of Energy\u2019s Office of Science<\/strong>\u00a0is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit\u00a0https:\/\/\u200bener\u200bgy\u200b.gov\/\u200bs\u200bc\u200bience<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"Artificial intelligence\u00a0(AI) is transforming nearly every branch of science. And researchers at the U.S. Department of Energy\u2019s (DOE)…\n","protected":false},"author":2,"featured_media":364136,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[77],"tags":[169373,61338,169372,18,19,17,941,133],"class_list":{"0":"post-364135","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-science","8":"tag-artifical-intelligencex-ray-science-and-technologysynchrotron-sciencescientific-user-facilities","9":"tag-argonne-national-laboratory","10":"tag-computational-sciencecomputational-materials-sciencecomputer-science-and-engineering","11":"tag-eire","12":"tag-ie","13":"tag-ireland","14":"tag-newswise","15":"tag-science"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@ie\/116160481375376241","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/364135","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/comments?post=364135"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/posts\/364135\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media\/364136"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/media?parent=364135"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/categories?post=364135"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ie\/wp-json\/wp\/v2\/tags?post=364135"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}