{"id":8531,"date":"2023-12-20T16:29:30","date_gmt":"2023-12-20T16:29:30","guid":{"rendered":"https:\/\/dailyai.com\/?p=8531"},"modified":"2023-12-21T10:38:38","modified_gmt":"2023-12-21T10:38:38","slug":"ml-boosts-x-ray-diffraction-techniques-to-find-new-materials","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2023\/12\/ml-boosts-x-ray-diffraction-techniques-to-find-new-materials\/","title":{"rendered":"ML styrker r\u00f8ntgendiffraksjonsteknikker for \u00e5 finne nye materialer"},"content":{"rendered":"<p><strong>Materialforskere pr\u00f8ver hele tiden \u00e5 finne nye materialer med spesifikke egenskaper, men de eksperimentelle dataene de m\u00e5 g\u00e5 gjennom, er overveldende. Forskere ved University of Rochester brukte maskinl\u00e6ring for \u00e5 finne nye materialer raskere.<\/strong><\/p>\n<p>Krystallinske materialer har en velordnet, repeterende krystallgitterstruktur, et regelmessig, repeterende arrangement av atomer, ioner eller molekyler. Det er arrangementet av disse krystallgitrene som gir et materiale spesifikke egenskaper.<\/p>\n<p>Vil du ha et materiale som er hardt, t\u00e5ler h\u00f8ye temperaturer og er lett? Da m\u00e5 du finne den rette gitterstrukturen for \u00e5 f\u00e5 det til.<\/p>\n<p>N\u00e5r materialforskere syntetiserer en liten mengde av et nytt materiale, \u00f8nsker de \u00e5 vite hva slags egenskaper det vil ha, slik at de kan avgj\u00f8re om det er egnet til et bestemt bruksomr\u00e5de eller ikke.<\/p>\n<p>De bruker en prosess som kalles r\u00f8ntgendiffraksjon (XRD) for \u00e5 gj\u00f8re dette. Materialpr\u00f8ven males vanligvis til et fint pulver og eksponeres deretter for r\u00f8ntgenstr\u00e5ler. N\u00e5r r\u00f8ntgenstr\u00e5lene treffer atomene i materialet, blir de diffraktert i ulike retninger, avhengig av hvordan atomene er ordnet.<\/p>\n<p>De diffrakterte r\u00f8ntgenstr\u00e5lene skaper et m\u00f8nster p\u00e5 en detektor som forskerne m\u00e5 analysere for \u00e5 finne ut hvilke egenskaper materialet har. Problemet er at r\u00f8ntgenanalyse produserer en enorm mengde data som mennesker ikke er i stand til \u00e5 behandle p\u00e5 en effektiv m\u00e5te.<\/p>\n<h2>Automatisering av materialanalyse<\/h2>\n<p><a href=\"https:\/\/www.nature.com\/articles\/s41524-023-01164-8\" target=\"_blank\" rel=\"noopener\">Studien<\/a>ledet av doktorgradsstipendiat i materialvitenskap <a href=\"https:\/\/www.hajim.rochester.edu\/me\/sites\/abdolrahim\/people\/graduate-students\/jerardo-salgado\/index.html\" target=\"_blank\" rel=\"noopener\">Jerardo Salgado<\/a>utviklet dypl\u00e6ringsmodeller for \u00e5 automatisere klassifiseringen av materialer basert p\u00e5 XRD-m\u00f8nstrene deres.<\/p>\n<p>Maskinl\u00e6ringsmodellene de brukte, benytter konvolusjonale nevrale nettverk (CNN), en type nevrale nettverk som er veldig gode til \u00e5 utf\u00f8re bildegjenkjenning og klassifiseringsoppgaver.<\/p>\n<p>Modellene ble trent p\u00e5 et stort datasett med syntetiske XRD-m\u00f8nstre, som ble generert for \u00e5 representere et bredt spekter av eksperimentelle forhold og materialtyper.<\/p>\n<p>Prosjektleder <a href=\"https:\/\/www.hajim.rochester.edu\/me\/people\/faculty\/abdolrahim_niaz\/index.html\" target=\"_blank\" rel=\"noopener\">Niaz Abdolrahim<\/a>\"Det ligger mye materialvitenskap og fysikk gjemt i hvert eneste av disse bildene, og hver dag produseres det terabytes med data ved anlegg og laboratorier over hele verden\", sier professor i maskinteknikk ved University of Rochester.<\/p>\n<p>Abdolrahim forklarer fordelene med maskinl\u00e6ring p\u00e5 sitt felt: \"\u00c5 utvikle en god modell for \u00e5 analysere disse dataene kan virkelig bidra til \u00e5 fremskynde materialinnovasjon, forst\u00e5 materialer under ekstreme forhold og utvikle materialer for ulike teknologiske bruksomr\u00e5der.\"<\/p>\n<p>Ved \u00e5 bruke maskinl\u00e6ringsmodeller til \u00e5 filtrere XRD-data kan man fremskynde utviklingen av raskere elektronikk, bedre batterier eller til og med dagligdagse gjenstander med forbedret holdbarhet, funksjonalitet eller b\u00e6rekraft.<\/p>\n<p>Forskere fra <a href=\"https:\/\/cmap.rochester.edu\/\" target=\"_blank\" rel=\"noopener\">Senter for materie ved atomtrykk<\/a> har en spesiell interesse for denne anvendelsen av maskinl\u00e6ring. Ved \u00e5 bruke XRD mens materialer utsettes for ekstreme trykk og temperaturer, kan forskere ikke bare finne nye m\u00e5ter \u00e5 skape nye materialer p\u00e5, men ogs\u00e5 l\u00e6re mer om dannelsen av stjerner og planeter.<\/p>\n<p>Ved \u00e5 bruke kunstig intelligens til \u00e5 frigj\u00f8re vitenskapelige hjerner fra det slitsomme arbeidet med dataanalyse, vil de kunne bruke sin kreative tenkning til \u00e5 designe materialer som vil forme fremtiden v\u00e5r.<\/p>","protected":false},"excerpt":{"rendered":"<p>Materialforskere pr\u00f8ver hele tiden \u00e5 finne nye materialer med spesifikke egenskaper, men de eksperimentelle dataene de m\u00e5 g\u00e5 gjennom, er overveldende. Forskere ved University of Rochester brukte maskinl\u00e6ring for \u00e5 finne nye materialer raskere. Krystallinske materialer har en velordnet, repeterende krystallgitterstruktur, et regelmessig, repeterende arrangement av atomer, ioner eller molekyler. Det er plasseringen av disse krystallgitrene som gir et materiale spesifikke egenskaper. Vil du ha et materiale som er hardt, t\u00e5ler h\u00f8ye temperaturer og er lett? Da m\u00e5 du ha akkurat den rette gitterstrukturen for \u00e5 f\u00e5 det til. N\u00e5r materialforskere syntetiserer en liten mengde<\/p>","protected":false},"author":6,"featured_media":8534,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[150,105],"class_list":["post-8531","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai-benefits","tag-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>ML boosts X-ray diffraction techniques to find new materials | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/nb\/2023\/12\/ml-boosts-x-ray-diffraction-techniques-to-find-new-materials\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ML boosts X-ray diffraction techniques to find new materials | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Material scientists are constantly trying to find new materials with specific properties but the experimental data they have to wade through is overwhelming. Scientists at the University of Rochester used machine learning to fast-track new materials discovery. Crystalline materials have a well-ordered, repeating crystal lattice structure, a regular, repeating arrangement of atoms, ions, or molecules. The arrangement of these crystal lattices is what gives a material specific properties. Want a material that\u2019s hard, handles high temperatures, and is lightweight? You\u2019ve got to get just the right lattice structure to make that happen. When material scientists synthesize a small amount of\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/nb\/2023\/12\/ml-boosts-x-ray-diffraction-techniques-to-find-new-materials\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2023-12-20T16:29:30+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-12-21T10:38:38+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/12\/material-crystal-lattice.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1792\" \/>\n\t<meta property=\"og:image:height\" content=\"1024\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Eugene van der Watt\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:site\" content=\"@DailyAIOfficial\" \/>\n<meta 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