{"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\/it\/2023\/12\/ml-boosts-x-ray-diffraction-techniques-to-find-new-materials\/","title":{"rendered":"ML potenzia le tecniche di diffrazione dei raggi X per trovare nuovi materiali"},"content":{"rendered":"

Gli scienziati dei materiali cercano costantemente di trovare nuovi materiali con propriet\u00e0 specifiche, ma i dati sperimentali che devono analizzare sono schiaccianti. Gli scienziati dell'Universit\u00e0 di Rochester hanno utilizzato l'apprendimento automatico per accelerare la scoperta di nuovi materiali.<\/strong><\/p>\n

I materiali cristallini hanno una struttura reticolare cristallina ben ordinata e ripetuta, una disposizione regolare e ripetuta di atomi, ioni o molecole. La disposizione di questi reticoli cristallini conferisce al materiale propriet\u00e0 specifiche.<\/p>\n

Volete un materiale che sia duro, resistente alle alte temperature e leggero? Per farlo, \u00e8 necessario ottenere la giusta struttura reticolare.<\/p>\n

Quando gli scienziati dei materiali sintetizzano una piccola quantit\u00e0 di un nuovo materiale, vogliono sapere che tipo di propriet\u00e0 avr\u00e0 per decidere se \u00e8 fattibile o meno per una particolare applicazione.<\/p>\n

Per farlo, utilizzano un processo chiamato diffrazione dei raggi X (XRD). Il campione di materiale viene normalmente macinato in una polvere fine e poi esposto ai raggi X. I raggi X colpiscono gli atomi del materiale e vengono diffratti in varie direzioni, a seconda della disposizione degli atomi. Quando i raggi X colpiscono gli atomi del materiale, vengono diffratti in varie direzioni, a seconda della disposizione atomica.<\/p>\n

I raggi X diffratti creano un modello su un rivelatore che gli scienziati devono analizzare per dedurre le propriet\u00e0 del materiale. Il problema \u00e8 che la XRD produce un'enorme quantit\u00e0 di dati che l'uomo non \u00e8 in grado di elaborare in modo efficace.<\/p>\n

Automatizzazione dell'analisi dei materiali<\/h2>\n

Lo studio<\/a>guidato da uno studente di dottorato in scienze dei materiali Jerardo Salgado<\/a>ha sviluppato modelli di deep learning per automatizzare la classificazione dei materiali in base ai loro modelli XRD.<\/p>\n

I modelli di apprendimento automatico utilizzati impiegano le reti neurali convoluzionali (CNN), un tipo di rete neurale molto efficace nel riconoscimento e nella classificazione delle immagini.<\/p>\n

I modelli sono stati addestrati su un ampio set di modelli XRD sintetici, generati per rappresentare un'ampia gamma di condizioni sperimentali e tipi di materiali.<\/p>\n

Responsabile del progetto Niaz Abdolrahim<\/a>Il professore di ingegneria meccanica dell'Universit\u00e0 di Rochester ha dichiarato: \"In ognuna di queste immagini si nasconde una grande quantit\u00e0 di scienza dei materiali e di fisica, e ogni giorno vengono prodotti terabyte di dati in strutture e laboratori di tutto il mondo\".<\/p>\n

Spiegando i vantaggi dell'apprendimento automatico nel suo campo, Abdolrahim ha detto: \"Lo sviluppo di un buon modello per analizzare questi dati pu\u00f2 davvero aiutare ad accelerare l'innovazione dei materiali, a comprendere i materiali in condizioni estreme e a sviluppare materiali per diverse applicazioni tecnologiche\".<\/p>\n

L'utilizzo di modelli di apprendimento automatico per filtrare i dati XRD potrebbe accelerare lo sviluppo di elettronica pi\u00f9 veloce, di batterie migliori o persino di oggetti di uso quotidiano con maggiore durata, funzionalit\u00e0 o sostenibilit\u00e0.<\/p>\n

I ricercatori del Centro per la materia a pressione atomica<\/a> hanno un interesse particolare per questa applicazione dell'apprendimento automatico. L'uso della XRD durante l'esposizione dei materiali a pressioni e temperature estreme aiuter\u00e0 gli scienziati non solo a scoprire come creare nuovi materiali, ma anche a conoscere la formazione di stelle e pianeti.<\/p>\n

L'utilizzo dell'intelligenza artificiale per liberare le menti scientifiche dalla fatica dell'analisi dei dati consentir\u00e0 di indirizzare meglio il loro pensiero creativo verso la progettazione dei materiali che daranno forma al nostro futuro.<\/p>","protected":false},"excerpt":{"rendered":"

Gli scienziati dei materiali cercano costantemente di trovare nuovi materiali con propriet\u00e0 specifiche, ma i dati sperimentali che devono analizzare sono schiaccianti. Gli scienziati dell'Universit\u00e0 di Rochester hanno utilizzato l'apprendimento automatico per accelerare la scoperta di nuovi materiali. I materiali cristallini hanno una struttura reticolare cristallina ben ordinata e ripetuta, una disposizione regolare e ripetuta di atomi, ioni o molecole. La disposizione di questi reticoli cristallini conferisce al materiale propriet\u00e0 specifiche. Volete un materiale duro, resistente alle alte temperature e leggero? Bisogna ottenere la giusta struttura reticolare per riuscirci. Quando gli scienziati dei materiali sintetizzano una piccola quantit\u00e0 di<\/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\/it\/2023\/12\/ml-boosts-x-ray-diffraction-techniques-to-find-new-materials\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\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. 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