{"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\/nl\/2023\/12\/ml-boosts-x-ray-diffraction-techniques-to-find-new-materials\/","title":{"rendered":"ML verbetert r\u00f6ntgendiffractietechnieken om nieuwe materialen te vinden"},"content":{"rendered":"<p><strong>Materiaalwetenschappers proberen voortdurend nieuwe materialen met specifieke eigenschappen te vinden, maar de experimentele gegevens die ze moeten doorworstelen zijn overweldigend. Wetenschappers van de Universiteit van Rochester gebruikten machine learning om snel nieuwe materialen te ontdekken.<\/strong><\/p>\n<p>Kristallijne materialen hebben een goed geordende, zich herhalende kristalroosterstructuur, een regelmatige, zich herhalende rangschikking van atomen, ionen of moleculen. De ordening van deze kristalroosters geeft een materiaal specifieke eigenschappen.<\/p>\n<p>Wil je een materiaal dat hard is, hoge temperaturen aankan en licht van gewicht is? Dan heb je precies de juiste rasterstructuur nodig.<\/p>\n<p>Wanneer materiaalwetenschappers een kleine hoeveelheid van een nieuw materiaal synthetiseren, willen ze weten welke eigenschappen het zal hebben om te beslissen of het al dan niet geschikt is voor een bepaalde toepassing.<\/p>\n<p>Hiervoor gebruiken ze een proces dat r\u00f6ntgendiffractie (XRD) heet. Het materiaalmonster wordt normaal gesproken vermalen tot een fijn poeder en vervolgens blootgesteld aan r\u00f6ntgenstraling. Als de r\u00f6ntgenstralen de atomen in het materiaal raken, worden ze in verschillende richtingen gebroken, afhankelijk van de atomaire rangschikking.<\/p>\n<p>De gebroken r\u00f6ntgenstralen cre\u00ebren een patroon op een detector dat de wetenschappers moeten analyseren om de eigenschappen van het materiaal af te leiden. Het probleem is dat XRD een enorme hoeveelheid gegevens produceert die mensen niet effectief kunnen verwerken.<\/p>\n<h2>Materiaalanalyse automatiseren<\/h2>\n<p><a href=\"https:\/\/www.nature.com\/articles\/s41524-023-01164-8\" target=\"_blank\" rel=\"noopener\">De studie<\/a>onder leiding van promovendus materiaalwetenschappen <a href=\"https:\/\/www.hajim.rochester.edu\/me\/sites\/abdolrahim\/people\/graduate-students\/jerardo-salgado\/index.html\" target=\"_blank\" rel=\"noopener\">Jerardo Salgado<\/a>heeft deep learning-modellen ontwikkeld om de classificatie van materialen op basis van hun XRD-patronen te automatiseren.<\/p>\n<p>De machine-learning modellen die ze gebruikten maken gebruik van convolutionele neurale netwerken (CNN's), een type neuraal netwerk dat erg goed is in het uitvoeren van beeldherkennings- en classificatietaken.<\/p>\n<p>De modellen werden getraind op een grote dataset van synthetische XRD-patronen, die werden gegenereerd om een breed scala aan experimentele omstandigheden en materiaalsoorten te vertegenwoordigen.<\/p>\n<p>Projectleider <a href=\"https:\/\/www.hajim.rochester.edu\/me\/people\/faculty\/abdolrahim_niaz\/index.html\" target=\"_blank\" rel=\"noopener\">Niaz Abdolrahim<\/a>, een professor werktuigbouwkunde aan de Universiteit van Rochester, zei: \"In elk van deze beelden zit veel materiaalwetenschap en natuurkunde verborgen en er worden elke dag terabytes aan gegevens geproduceerd in faciliteiten en laboratoria over de hele wereld.\"<\/p>\n<p>Abdolrahim legde de voordelen van machine learning in zijn vakgebied uit: \"Het ontwikkelen van een goed model om deze gegevens te analyseren kan echt helpen bij het versnellen van materiaalinnovatie, het begrijpen van materialen onder extreme omstandigheden en het ontwikkelen van materialen voor verschillende technologische toepassingen.\"<\/p>\n<p>Het gebruik van modellen voor machinaal leren om XRD-gegevens te filteren zou de ontwikkeling van snellere elektronica, betere batterijen of zelfs alledaagse voorwerpen met verbeterde duurzaamheid, functionaliteit of duurzaamheid kunnen versnellen.<\/p>\n<p>Onderzoekers van de <a href=\"https:\/\/cmap.rochester.edu\/\" target=\"_blank\" rel=\"noopener\">Centrum voor materie bij atoomdruk<\/a> zijn bijzonder ge\u00efnteresseerd in deze toepassing van machinaal leren. Door XRD te gebruiken terwijl materialen worden blootgesteld aan extreme drukken en temperaturen kunnen wetenschappers niet alleen manieren ontdekken om nieuwe materialen te maken, maar ook meer te weten komen over de vorming van sterren en planeten.<\/p>\n<p>Door AI te gebruiken om wetenschappelijke geesten te bevrijden van de sleur van gegevensanalyse zal hun creatieve denken beter gericht zijn op het ontwerpen van de materialen die onze toekomst vorm zullen geven.<\/p>","protected":false},"excerpt":{"rendered":"<p>Materiaalwetenschappers proberen voortdurend nieuwe materialen met specifieke eigenschappen te vinden, maar de experimentele gegevens die ze moeten doorworstelen zijn overweldigend. Wetenschappers van de Universiteit van Rochester gebruikten machine learning om snel nieuwe materialen te ontdekken. Kristallijne materialen hebben een goed geordende, zich herhalende kristalroosterstructuur, een regelmatige, zich herhalende rangschikking van atomen, ionen of moleculen. De ordening van deze kristalroosters geeft een materiaal specifieke eigenschappen. Wil je een materiaal dat hard is, bestand tegen hoge temperaturen en licht van gewicht? Dan moet je precies de juiste roosterstructuur hebben om dat voor elkaar te krijgen. Wanneer materiaalwetenschappers een kleine hoeveelheid<\/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 v27.3 - 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\/nl\/2023\/12\/ml-boosts-x-ray-diffraction-techniques-to-find-new-materials\/\" \/>\n<meta property=\"og:locale\" content=\"nl_NL\" \/>\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. 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