{"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\/fr\/2023\/12\/ml-boosts-x-ray-diffraction-techniques-to-find-new-materials\/","title":{"rendered":"ML renforce les techniques de diffraction des rayons X pour trouver de nouveaux mat\u00e9riaux"},"content":{"rendered":"

Les sp\u00e9cialistes des mat\u00e9riaux tentent constamment de trouver de nouveaux mat\u00e9riaux dot\u00e9s de propri\u00e9t\u00e9s sp\u00e9cifiques, mais les donn\u00e9es exp\u00e9rimentales qu'ils doivent traiter sont \u00e9crasantes. Des scientifiques de l'universit\u00e9 de Rochester ont utilis\u00e9 l'apprentissage automatique pour acc\u00e9l\u00e9rer la d\u00e9couverte de nouveaux mat\u00e9riaux.<\/strong><\/p>\n

Les mat\u00e9riaux cristallins ont une structure de r\u00e9seau cristallin bien ordonn\u00e9e et r\u00e9p\u00e9titive, un arrangement r\u00e9gulier et r\u00e9p\u00e9titif d'atomes, d'ions ou de mol\u00e9cules. La disposition de ces r\u00e9seaux cristallins conf\u00e8re au mat\u00e9riau des propri\u00e9t\u00e9s sp\u00e9cifiques.<\/p>\n

Vous voulez un mat\u00e9riau dur, r\u00e9sistant aux temp\u00e9ratures \u00e9lev\u00e9es et l\u00e9ger ? Pour y parvenir, il faut obtenir la bonne structure de treillis.<\/p>\n

Lorsque les sp\u00e9cialistes des mat\u00e9riaux synth\u00e9tisent une petite quantit\u00e9 d'un nouveau mat\u00e9riau, ils veulent savoir quels types de propri\u00e9t\u00e9s il aura pour d\u00e9cider s'il est viable ou non pour une application particuli\u00e8re.<\/p>\n

Pour ce faire, ils utilisent un proc\u00e9d\u00e9 appel\u00e9 diffraction des rayons X (DRX). L'\u00e9chantillon de mat\u00e9riau est normalement broy\u00e9 en une fine poudre, puis expos\u00e9 aux rayons X. Lorsque les rayons X atteignent les atomes du mat\u00e9riau, ils sont diffract\u00e9s dans diff\u00e9rentes directions, en fonction de l'arrangement atomique. Lorsque les rayons X atteignent les atomes du mat\u00e9riau, ils sont diffract\u00e9s dans diff\u00e9rentes directions, en fonction de l'arrangement atomique.<\/p>\n

Les rayons X diffract\u00e9s cr\u00e9ent un motif sur un d\u00e9tecteur que les scientifiques doivent analyser pour en d\u00e9duire les propri\u00e9t\u00e9s du mat\u00e9riau. Le probl\u00e8me est que la DRX produit une \u00e9norme quantit\u00e9 de donn\u00e9es que l'homme n'est pas en mesure de traiter efficacement.<\/p>\n

Automatisation de l'analyse des mat\u00e9riaux<\/h2>\n

L'\u00e9tude<\/a>sous la direction d'un doctorant en science des mat\u00e9riaux Jerardo Salgado<\/a>a d\u00e9velopp\u00e9 des mod\u00e8les d'apprentissage profond pour automatiser la classification des mat\u00e9riaux sur la base de leurs mod\u00e8les XRD.<\/p>\n

Les mod\u00e8les d'apprentissage automatique qu'ils ont utilis\u00e9s emploient des r\u00e9seaux neuronaux convolutifs (CNN), un type de r\u00e9seau neuronal tr\u00e8s performant pour les t\u00e2ches de reconnaissance et de classification d'images.<\/p>\n

Les mod\u00e8les ont \u00e9t\u00e9 entra\u00een\u00e9s sur un vaste ensemble de donn\u00e9es de mod\u00e8les XRD synth\u00e9tiques, g\u00e9n\u00e9r\u00e9s pour repr\u00e9senter une large gamme de conditions exp\u00e9rimentales et de types de mat\u00e9riaux.<\/p>\n

Chef de projet Niaz Abdolrahim<\/a>professeur de g\u00e9nie m\u00e9canique \u00e0 l'universit\u00e9 de Rochester, a d\u00e9clar\u00e9 : \"Une grande partie de la science des mat\u00e9riaux et de la physique se cache dans chacune de ces images, et des t\u00e9raoctets de donn\u00e9es sont produits chaque jour dans des installations et des laboratoires du monde entier.<\/p>\n

Expliquant les avantages de l'apprentissage automatique dans son domaine, M. Abdolrahim a d\u00e9clar\u00e9 : \"Le d\u00e9veloppement d'un bon mod\u00e8le pour analyser ces donn\u00e9es peut vraiment aider \u00e0 acc\u00e9l\u00e9rer l'innovation dans le domaine des mat\u00e9riaux, \u00e0 comprendre les mat\u00e9riaux dans des conditions extr\u00eames et \u00e0 d\u00e9velopper des mat\u00e9riaux pour diff\u00e9rentes applications technologiques.\"<\/p>\n

L'utilisation de mod\u00e8les d'apprentissage automatique pour filtrer les donn\u00e9es XRD pourrait acc\u00e9l\u00e9rer le d\u00e9veloppement d'appareils \u00e9lectroniques plus rapides, de meilleures batteries ou m\u00eame d'articles de tous les jours dont la durabilit\u00e9, la fonctionnalit\u00e9 ou la viabilit\u00e9 seraient am\u00e9lior\u00e9es.<\/p>\n

Les chercheurs de l Centre pour la mati\u00e8re \u00e0 pression atomique<\/a> s'int\u00e9ressent particuli\u00e8rement \u00e0 cette application de l'apprentissage automatique. L'utilisation de la XRD lors de l'exposition de mat\u00e9riaux \u00e0 des pressions et des temp\u00e9ratures extr\u00eames aidera les scientifiques non seulement \u00e0 d\u00e9couvrir des moyens de cr\u00e9er de nouveaux mat\u00e9riaux, mais aussi \u00e0 en savoir plus sur la formation des \u00e9toiles et des plan\u00e8tes.<\/p>\n

L'utilisation de l'IA pour lib\u00e9rer les esprits scientifiques de la corv\u00e9e de l'analyse des donn\u00e9es permettra de mieux orienter leur pens\u00e9e cr\u00e9ative vers la conception des mat\u00e9riaux qui fa\u00e7onneront notre avenir.<\/p>","protected":false},"excerpt":{"rendered":"

Les sp\u00e9cialistes des mat\u00e9riaux tentent constamment de trouver de nouveaux mat\u00e9riaux dot\u00e9s de propri\u00e9t\u00e9s sp\u00e9cifiques, mais les donn\u00e9es exp\u00e9rimentales qu'ils doivent traiter sont \u00e9crasantes. Des scientifiques de l'universit\u00e9 de Rochester ont utilis\u00e9 l'apprentissage automatique pour acc\u00e9l\u00e9rer la d\u00e9couverte de nouveaux mat\u00e9riaux. Les mat\u00e9riaux cristallins pr\u00e9sentent une structure de r\u00e9seau cristallin bien ordonn\u00e9e et r\u00e9p\u00e9titive, c'est-\u00e0-dire un arrangement r\u00e9gulier et r\u00e9p\u00e9titif d'atomes, d'ions ou de mol\u00e9cules. C'est la disposition de ces r\u00e9seaux cristallins qui conf\u00e8re au mat\u00e9riau ses propri\u00e9t\u00e9s sp\u00e9cifiques. Vous voulez un mat\u00e9riau dur, qui r\u00e9siste \u00e0 des temp\u00e9ratures \u00e9lev\u00e9es et qui soit l\u00e9ger ? Il faut obtenir la bonne structure de r\u00e9seau pour y parvenir. Lorsque les sp\u00e9cialistes des mat\u00e9riaux synth\u00e9tisent une petite quantit\u00e9 de<\/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\/fr\/2023\/12\/ml-boosts-x-ray-diffraction-techniques-to-find-new-materials\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\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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