{"id":699,"date":"2020-01-22T13:30:52","date_gmt":"2020-01-22T13:30:52","guid":{"rendered":"https:\/\/website.prod.unilu.spikeseed.cloud\/fr\/news\/big-data-at-the-nanoscale\/"},"modified":"2020-01-22T13:30:52","modified_gmt":"2020-01-22T13:30:52","slug":"big-data-at-the-nanoscale","status":"publish","type":"news","link":"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/","title":{"rendered":"Big Data at the Nanoscale"},"content":{"rendered":"<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><p>An international team of scientists, including physicists from the University of Luxembourg, have reported a comprehensive view-point on how machine learning approaches can be used in Nanoscience to analyse and extract new insights from large data sets, and accelerate material discovery, and to guide experimental design. Moreover, they discuss some of the main physical challenging behind the realisation of tailored memristive devices for machine learning.<\/p><p>The researchers have published a Mini Review in\u00a0<a href=\"https:\/\/pubs.acs.org\/journal\/nalefd\" target=\"_blank\" title=\"\" rel=\"noopener\">Nano Letters<\/a>, the prestigious journal of the American Chemical Society committed to publishing key advances in fundamental nanoscience. The article was produced in cooperation with researchers at the <a href=\"http:\/\/www.bu.edu\/\" target=\"_blank\" title=\"\" rel=\"noopener\">University of Boston<\/a>, the <a href=\"https:\/\/home.www.upenn.edu\/\" target=\"_blank\" title=\"\" rel=\"noopener\">University of Pennsylvania<\/a>, the <a href=\"https:\/\/www.nrl.navy.mil\/\" target=\"_blank\" title=\"\" rel=\"noopener\">US Naval Research Laboratory<\/a>, and the <a href=\"https:\/\/erc.europa.eu\/interuniversity-microelectronics-centre-imec\" target=\"_blank\" title=\"\" rel=\"noopener\">Interuniversity Microelectronics Centre<\/a>\u00a0(Belgium), the world-leading R&#038;D and innovation hub in nanoelectronics and digital technologies.<\/p><figure class=\"wp-block-dev4-reusable-blocks-image  object-fit--contain\">\n    \n<img decoding=\"async\" class=\"wp-block-image unilux-custom-image-block\"\n                alt=\"\"\n            src=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2023\/07\/machine.jpg\"\n                    style=\"object-position: 50.00% 50.00%; font-family: &quot;object-fit: contain; object-position: 50.00% 50.00%;&quot;; aspect-ratio: 16\/9; object-fit: contain; width: 100%;\"\n        loading=\"lazy\"\n\/>    <\/figure><p>In nanoscience, high-throughput experiments enabled by the small size of nanoscale samples and rapid, high-resolution imaging tools are becoming increasingly widespread. For example, in nanophotonics and catalysis material properties have been varied systematically across the same wafer-sized substrate and characterised locally using high-resolution scanning probe and optical or electron micro-spectroscopy techniques. These or similar methods can generate data sets that are too vast and complex for researchers to mentally parse without computational assistance; yet, these data are rich in relationships that the researchers would like to understand. In this framework, <strong>machine learning<\/strong> enables researchers to <strong>analyse large data sets<\/strong> by training models that can be used to <strong>classify observations into discrete groups<\/strong>, learn which features determine a metric of performance, or predict the outcome of new experiments. Furthermore, machine learning can assist researchers in designing experiments to optimise performance or test hypotheses more effectively. \u201cFrom nano-optoelectronics, to catalysis, to the bio-nano interface, machine learning is reshaping how researchers collect, analyse, and interpret their data\u201d says <a href=\"https:\/\/wwwfr.uni.lu\/recherche\/fstm\/dphyms\/people\/nicolo_maccaferri\" target=\"_self\" title=\"\" rel=\"noopener\">Nicol\u00f2 Maccaferri<\/a>, Researcher at the Department of Physics and Materials Science (DPHYMS) of the University of Luxembourg.<\/p><p>\u201cIn the upcoming years, data-driven science will be fundamental for the discovery and the design of new materials which can help us to increase the efficiency of a plethora of processes, from chemistry to electronics\u201d explains Maccaferri. Within the digital strategy of University of Luxembourg, machine learning approaches will help in this direction. \u201cThese methodologies can help experimentalists to advance faster in designing experiments and to process and interpret their data.\u00a0\u00ab\u00a0In our particular case, using machine learning we can analyse and process the large amount of information encoded in the optical spectra of nanostructures we study in our laboratory, thus enabling quasi-error-free data readout. At the same time, we can use these data for the inverse design and optimisation of photonic nanostructures which can be used for developing post-CMOS devices and systems beyond von Neumann architectures. In this paradigm shift the wave nature of light and related inherent operations, such as interference and diffraction, can play a major role in enhancing computational throughput of machine learning approaches\u201d conclude Maccaferri, who is also looking forward to actively collaborate with theoreticians and data scientists here at the University to develop new methodologies for improving the speed at which electronic components work.<\/p><p>Publication: <a href=\"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.nanolett.9b04090\" target=\"_blank\" title=\"\" rel=\"noopener\">Machine Learning in Nanoscience: Big Data at Small Scales<\/a>, Nano Letters, January 2020<\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"<p>An international team of scientists, including physicists from the University of Luxembourg, have reported a comprehensive view-point on how machine learning approaches can be used in Nanoscience to analyse and extract new insights from large data sets, and accelerate material discovery, and to guide experimental design. Moreover, they discuss some of the main physical challenging behind the realisation of tailored memristive devices for machine learning.<\/p>\n","protected":false},"author":0,"featured_media":0,"template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false},"news-category":[4,3],"news-topic":[21],"organisation":[76,25,226],"authorship":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Big Data at the Nanoscale - Universit\u00e9 du Luxembourg<\/title>\n<meta name=\"description\" content=\"An international team of scientists, including physicists from the University of Luxembourg, have reported a comprehensive view-point on how machine learning approaches can be used in Nanoscience to analyse and extract new insights from large data sets, and accelerate material discovery, and to guide experimental design. Moreover, they discuss some of the main physical challenging behind the realisation of tailored memristive devices for machine learning.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Big Data at the Nanoscale\" \/>\n<meta property=\"og:description\" content=\"An international team of scientists, including physicists from the University of Luxembourg, have reported a comprehensive view-point on how machine learning approaches can be used in Nanoscience to analyse and extract new insights from large data sets, and accelerate material discovery, and to guide experimental design. Moreover, they discuss some of the main physical challenging behind the realisation of tailored memristive devices for machine learning.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/\" \/>\n<meta property=\"og:site_name\" content=\"UNI FR\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/uni.lu\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2020\/01\/big_data_at_the_nanoscale.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"800\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data1\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"NewsArticle\",\"@id\":\"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/\"},\"author\":{\"name\":\"\",\"@id\":\"\"},\"headline\":\"Big Data at the Nanoscale\",\"datePublished\":\"2020-01-22T13:30:52+00:00\",\"dateModified\":\"2020-01-22T13:30:52+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/\"},\"wordCount\":531,\"publisher\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/#organization\"},\"inLanguage\":\"fr-FR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/\",\"url\":\"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/\",\"name\":\"Big Data at the Nanoscale - Universit\u00e9 du Luxembourg\",\"isPartOf\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/#website\"},\"datePublished\":\"2020-01-22T13:30:52+00:00\",\"dateModified\":\"2020-01-22T13:30:52+00:00\",\"description\":\"An international team of scientists, including physicists from the University of Luxembourg, have reported a comprehensive view-point on how machine learning approaches can be used in Nanoscience to analyse and extract new insights from large data sets, and accelerate material discovery, and to guide experimental design. Moreover, they discuss some of the main physical challenging behind the realisation of tailored memristive devices for machine learning.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.uni.lu\/fr\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"News\",\"item\":\"https:\/\/www.uni.lu\/fr\/news\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Big Data at the Nanoscale\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.uni.lu\/fr\/#website\",\"url\":\"https:\/\/www.uni.lu\/fr\/\",\"name\":\"Uni.lu\",\"description\":\"Universit\u00e9 du Luxembourg\",\"publisher\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/#organization\"},\"alternateName\":\"Universit\u00e9 du Luxembourg\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.uni.lu\/fr\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.uni.lu\/fr\/#organization\",\"name\":\"Universit\u00e9 du Luxembourg\",\"alternateName\":\"Uni.lu\",\"url\":\"https:\/\/www.uni.lu\/fr\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/www.uni.lu\/fr\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg\",\"contentUrl\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg\",\"width\":2560,\"height\":2560,\"caption\":\"Universit\u00e9 du Luxembourg\"},\"image\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/uni.lu\",\"https:\/\/www.linkedin.com\/school\/university-of-luxembourg\/\",\"https:\/\/www.instagram.com\/uni.lu\",\"https:\/\/www.youtube.com\/@uni_lu\",\"https:\/\/en.wikipedia.org\/wiki\/University_of_Luxembourg\"]}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Big Data at the Nanoscale - Universit\u00e9 du Luxembourg","description":"An international team of scientists, including physicists from the University of Luxembourg, have reported a comprehensive view-point on how machine learning approaches can be used in Nanoscience to analyse and extract new insights from large data sets, and accelerate material discovery, and to guide experimental design. Moreover, they discuss some of the main physical challenging behind the realisation of tailored memristive devices for machine learning.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/","og_locale":"fr_FR","og_type":"article","og_title":"Big Data at the Nanoscale","og_description":"An international team of scientists, including physicists from the University of Luxembourg, have reported a comprehensive view-point on how machine learning approaches can be used in Nanoscience to analyse and extract new insights from large data sets, and accelerate material discovery, and to guide experimental design. Moreover, they discuss some of the main physical challenging behind the realisation of tailored memristive devices for machine learning.","og_url":"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/","og_site_name":"UNI FR","article_publisher":"https:\/\/www.facebook.com\/uni.lu","og_image":[{"width":800,"height":600,"url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2020\/01\/big_data_at_the_nanoscale.jpg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","twitter_misc":{"Dur\u00e9e de lecture estim\u00e9e":"3 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"NewsArticle","@id":"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/#article","isPartOf":{"@id":"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/"},"author":{"name":"","@id":""},"headline":"Big Data at the Nanoscale","datePublished":"2020-01-22T13:30:52+00:00","dateModified":"2020-01-22T13:30:52+00:00","mainEntityOfPage":{"@id":"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/"},"wordCount":531,"publisher":{"@id":"https:\/\/www.uni.lu\/fr\/#organization"},"inLanguage":"fr-FR"},{"@type":"WebPage","@id":"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/","url":"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/","name":"Big Data at the Nanoscale - Universit\u00e9 du Luxembourg","isPartOf":{"@id":"https:\/\/www.uni.lu\/fr\/#website"},"datePublished":"2020-01-22T13:30:52+00:00","dateModified":"2020-01-22T13:30:52+00:00","description":"An international team of scientists, including physicists from the University of Luxembourg, have reported a comprehensive view-point on how machine learning approaches can be used in Nanoscience to analyse and extract new insights from large data sets, and accelerate material discovery, and to guide experimental design. Moreover, they discuss some of the main physical challenging behind the realisation of tailored memristive devices for machine learning.","breadcrumb":{"@id":"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.uni.lu\/fr\/news\/big-data-at-the-nanoscale\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.uni.lu\/fr\/"},{"@type":"ListItem","position":2,"name":"News","item":"https:\/\/www.uni.lu\/fr\/news\/"},{"@type":"ListItem","position":3,"name":"Big Data at the Nanoscale"}]},{"@type":"WebSite","@id":"https:\/\/www.uni.lu\/fr\/#website","url":"https:\/\/www.uni.lu\/fr\/","name":"Uni.lu","description":"Universit\u00e9 du Luxembourg","publisher":{"@id":"https:\/\/www.uni.lu\/fr\/#organization"},"alternateName":"Universit\u00e9 du Luxembourg","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.uni.lu\/fr\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"fr-FR"},{"@type":"Organization","@id":"https:\/\/www.uni.lu\/fr\/#organization","name":"Universit\u00e9 du Luxembourg","alternateName":"Uni.lu","url":"https:\/\/www.uni.lu\/fr\/","logo":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fr\/#\/schema\/logo\/image\/","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg","width":2560,"height":2560,"caption":"Universit\u00e9 du Luxembourg"},"image":{"@id":"https:\/\/www.uni.lu\/fr\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/uni.lu","https:\/\/www.linkedin.com\/school\/university-of-luxembourg\/","https:\/\/www.instagram.com\/uni.lu","https:\/\/www.youtube.com\/@uni_lu","https:\/\/en.wikipedia.org\/wiki\/University_of_Luxembourg"]}]}},"blog_id":11,"_links":{"self":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/news\/699"}],"collection":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/news"}],"about":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/types\/news"}],"version-history":[{"count":0,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/news\/699\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/media?parent=699"}],"wp:term":[{"taxonomy":"news-category","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/news-category?post=699"},{"taxonomy":"news-topic","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/news-topic?post=699"},{"taxonomy":"organisation","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/organisation?post=699"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}