{"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\/sv\/2023\/12\/ml-boosts-x-ray-diffraction-techniques-to-find-new-materials\/","title":{"rendered":"ML f\u00f6rb\u00e4ttrar r\u00f6ntgendiffraktionstekniker f\u00f6r att hitta nya material"},"content":{"rendered":"

Materialforskare f\u00f6rs\u00f6ker st\u00e4ndigt hitta nya material med specifika egenskaper, men de experimentella data som de m\u00e5ste g\u00e5 igenom \u00e4r \u00f6verv\u00e4ldigande. Forskare vid University of Rochester anv\u00e4nde maskininl\u00e4rning f\u00f6r att p\u00e5skynda uppt\u00e4ckten av nya material.<\/strong><\/p>\n

Kristallina material har en v\u00e4lordnad, repetitiv kristallgitterstruktur, ett regelbundet, repetitivt arrangemang av atomer, joner eller molekyler. Arrangemanget av dessa kristallgitter \u00e4r det som ger ett material specifika egenskaper.<\/p>\n

Vill du ha ett material som \u00e4r h\u00e5rt, klarar h\u00f6ga temperaturer och \u00e4r l\u00e4tt? D\u00e5 m\u00e5ste man ha precis r\u00e4tt gitterstruktur f\u00f6r att det ska bli verklighet.<\/p>\n

N\u00e4r materialforskare syntetiserar en liten m\u00e4ngd av ett nytt material vill de veta vilka egenskaper det kommer att ha f\u00f6r att kunna avg\u00f6ra om det \u00e4r anv\u00e4ndbart f\u00f6r en viss applikation eller inte.<\/p>\n

De anv\u00e4nder en process som kallas r\u00f6ntgendiffraktion (XRD) f\u00f6r att g\u00f6ra detta. Materialprovet mals normalt till ett fint pulver och uts\u00e4tts sedan f\u00f6r r\u00f6ntgenstr\u00e5lning. N\u00e4r r\u00f6ntgenstr\u00e5larna tr\u00e4ffar atomerna i materialet diffrakteras de i olika riktningar, beroende p\u00e5 hur atomerna \u00e4r ordnade.<\/p>\n

De diffrakterade r\u00f6ntgenstr\u00e5larna skapar ett m\u00f6nster p\u00e5 en detektor som forskarna beh\u00f6ver analysera f\u00f6r att dra slutsatser om vilka egenskaper materialet har. Problemet \u00e4r att XRD producerar en enorm m\u00e4ngd data som \u00e4r bortom m\u00e4nniskans f\u00f6rm\u00e5ga att bearbeta p\u00e5 ett effektivt s\u00e4tt.<\/p>\n

Automatisering av materialanalys<\/h2>\n

Studien<\/a>under ledning av doktoranden i materialvetenskap Jerardo Salgado<\/a>, utvecklade modeller f\u00f6r djupinl\u00e4rning f\u00f6r att automatisera klassificeringen av material baserat p\u00e5 deras XRD-m\u00f6nster.<\/p>\n

De maskininl\u00e4rningsmodeller som de anv\u00e4nde anv\u00e4nder sig av CNN (convolutional neural networks), en typ av neurala n\u00e4tverk som \u00e4r riktigt bra p\u00e5 att utf\u00f6ra bildigenk\u00e4nning och klassificeringsuppgifter.<\/p>\n

Modellerna tr\u00e4nades p\u00e5 ett stort dataset med syntetiska XRD-m\u00f6nster, som genererades f\u00f6r att representera ett brett spektrum av experimentella f\u00f6rh\u00e5llanden och materialtyper.<\/p>\n

Projektledare Niaz Abdolrahim<\/a>, professor i maskinteknik vid University of Rochester, s\u00e4ger: \"Det finns mycket materialvetenskap och fysik dold i var och en av dessa bilder, och terabyte av data produceras varje dag vid anl\u00e4ggningar och laboratorier \u00f6ver hela v\u00e4rlden.\"<\/p>\n

Abdolrahim f\u00f6rklarar f\u00f6rdelarna med maskininl\u00e4rning inom sitt omr\u00e5de: \"Att utveckla en bra modell f\u00f6r att analysera dessa data kan verkligen bidra till att p\u00e5skynda materialinnovation, f\u00f6rst\u00e5 material under extrema f\u00f6rh\u00e5llanden och utveckla material f\u00f6r olika tekniska till\u00e4mpningar.\"<\/p>\n

Genom att anv\u00e4nda maskininl\u00e4rningsmodeller f\u00f6r att filtrera XRD-data kan man p\u00e5skynda utvecklingen av snabbare elektronik, b\u00e4ttre batterier eller till och med vardagsf\u00f6rem\u00e5l med f\u00f6rb\u00e4ttrad h\u00e5llbarhet, funktionalitet eller h\u00e5llbarhet.<\/p>\n

Forskare fr\u00e5n Centrum f\u00f6r materia vid atom\u00e4ra tryck<\/a> har ett s\u00e4rskilt intresse f\u00f6r denna till\u00e4mpning av maskininl\u00e4rning. Genom att anv\u00e4nda XRD samtidigt som material uts\u00e4tts f\u00f6r extrema tryck och temperaturer kan forskare inte bara uppt\u00e4cka s\u00e4tt att skapa nya material utan ocks\u00e5 l\u00e4ra sig mer om hur stj\u00e4rnor och planeter bildas.<\/p>\n

Genom att anv\u00e4nda AI f\u00f6r att befria vetenskapsm\u00e4nnen fr\u00e5n dataanalysens slitsamma arbete kan deras kreativa t\u00e4nkande b\u00e4ttre riktas mot att utforma de material som kommer att forma v\u00e5r framtid.<\/p>","protected":false},"excerpt":{"rendered":"

Materialforskare f\u00f6rs\u00f6ker st\u00e4ndigt hitta nya material med specifika egenskaper, men de experimentella data som de m\u00e5ste g\u00e5 igenom \u00e4r \u00f6verv\u00e4ldigande. Forskare vid University of Rochester anv\u00e4nde maskininl\u00e4rning f\u00f6r att p\u00e5skynda uppt\u00e4ckten av nya material. Kristallina material har en v\u00e4lordnad, repeterande kristallgitterstruktur, ett regelbundet, repeterande arrangemang av atomer, joner eller molekyler. Arrangemanget av dessa kristallgitter \u00e4r det som ger ett material specifika egenskaper. Vill du ha ett material som \u00e4r h\u00e5rt, klarar h\u00f6ga temperaturer och \u00e4r l\u00e4tt? D\u00e5 m\u00e5ste du ha precis r\u00e4tt gitterstruktur f\u00f6r att det ska bli verklighet. N\u00e4r materialforskare syntetiserar en liten m\u00e4ngd av<\/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":"\nML 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\/sv\/2023\/12\/ml-boosts-x-ray-diffraction-techniques-to-find-new-materials\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\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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