{"id":2277,"date":"2023-04-19T16:17:25","date_gmt":"2023-04-19T14:17:25","guid":{"rendered":"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/"},"modified":"2023-04-19T16:17:25","modified_gmt":"2023-04-19T14:17:25","slug":"physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/","title":{"rendered":"Physics Colloquium:Large-scale machine-learning assisted discovery and characterization of materials"},"content":{"rendered":"<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><\/div><\/section>","protected":false},"excerpt":{"rendered":"<p>Physics ColloquiumWednesday, May 3rd 2023at 1:00 pmlunch at 12 pmCampus LimpertsbergB\u00e2timent des Sciences, Room BS 0.03Talk by Professor. Dr. Miguel MarquesMartin Luther University Halle-Wittenberg, GermanyLarge-scale machine-learning assisted discovery and characterization of materialsAbstract:In this talk we discuss our recent attempts to discover, characterize, and understand inorganic compounds using ab initio approaches accelerated by machine learning. We start by motivating why the search for new materials is nowadays one of the most pressing technological problems. Then we summarize our recent work in using crystal-graph attention neural networks for the prediction of materials properties. To train these networks, we curated a dataset of over 2 million density-functional calculations with consistent calculation parameters. Combining the data and the newly developed networks we have already scanned more than two thousand prototypes spanning a space of more than one billion materials and identified tens of thousands of theoretically stable compounds. We then discuss how simple, interpretable machine learning approaches can be used to understand complex material properties, such as the transition temperature of superconductors. Finally, we speculate which role machine learning will have in the future of materials science.About the speaker:Miguel Marques received his PhD degree in Physics from the University of Wuerzburg in 2000, working under the supervision of E.K.U.Gross in the field of density functional theory for superconductors. He then held several post-doctoral positions in Spain, Germany, and in France. From 2005 to 2007 he was assistant professor at the University of Coimbra in Portugal. From 2007 to 2014 he was CNRS researcher at the University of Lyon 1, and from 2014 to 2023 professor at the Martin-Luther University of Halle-Wittenberg. He is now professor at the Research Center Future Energy Materials and Systems of the Ruhr University Bochum. His current research interests include density functional theory, superconductivity, application of machine learning to materials science, etc. He authored more than 200 articles, and has edited three books published by Springer in the Lecture Notes in Physics series. He also organized several summer schools and international workshops, such as the Benasque School and International Workshop in TDDFT, that takes place in Benasque, Spain every second year.<\/p>\n","protected":false},"author":0,"featured_media":2278,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","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,"event_start_date":"2023-05-03 13:00:00","event_end_date":"2023-05-03 14:00:00","event_speaker_name":"Talk by Professor. Dr. Miguel Marques, Martin Luther University Halle-Wittenberg, Germany - Invited by Prof. Ludger Wirtz ","event_speaker_link":"","event_is_online":false,"event_location":"Campus Limpertsberg\r\nB\u00e2timent des Sciences, Room 003","event_street":"","event_location_link":"","event_zip_code":"","event_city":"","event_country":"LU"},"events-topic":[310],"events-type":[],"organisation":[75],"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>Physics Colloquium:Large-scale machine-learning assisted discovery and characterization of materials - FSTM I Uni.lu<\/title>\n<meta name=\"description\" content=\"Physics ColloquiumWednesday, May 3rd 2023at 1:00 pmlunch at 12 pmCampus LimpertsbergB\u00e2timent des Sciences, Room BS 0.03Talk by Professor. Dr. Miguel MarquesMartin Luther University Halle-Wittenberg, GermanyLarge-scale machine-learning assisted discovery and characterization of materialsAbstract:In this talk we discuss our recent attempts to discover, characterize, and understand inorganic compounds using ab initio approaches accelerated by machine learning. We start by motivating why the search for new materials is nowadays one of the most pressing technological problems. Then we summarize our recent work in using crystal-graph attention neural networks for the prediction of materials properties. To train these networks, we curated a dataset of over 2 million density-functional calculations with consistent calculation parameters. Combining the data and the newly developed networks we have already scanned more than two thousand prototypes spanning a space of more than one billion materials and identified tens of thousands of theoretically stable compounds. We then discuss how simple, interpretable machine learning approaches can be used to understand complex material properties, such as the transition temperature of superconductors. Finally, we speculate which role machine learning will have in the future of materials science.About the speaker:Miguel Marques received his PhD degree in Physics from the University of Wuerzburg in 2000, working under the supervision of E.K.U.Gross in the field of density functional theory for superconductors. He then held several post-doctoral positions in Spain, Germany, and in France. From 2005 to 2007 he was assistant professor at the University of Coimbra in Portugal. From 2007 to 2014 he was CNRS researcher at the University of Lyon 1, and from 2014 to 2023 professor at the Martin-Luther University of Halle-Wittenberg. He is now professor at the Research Center Future Energy Materials and Systems of the Ruhr University Bochum. His current research interests include density functional theory, superconductivity, application of machine learning to materials science, etc. He authored more than 200 articles, and has edited three books published by Springer in the Lecture Notes in Physics series. He also organized several summer schools and international workshops, such as the Benasque School and International Workshop in TDDFT, that takes place in Benasque, Spain every second year.\" \/>\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\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Physics Colloquium:Large-scale machine-learning assisted discovery and characterization of materials\" \/>\n<meta property=\"og:description\" content=\"Physics ColloquiumWednesday, May 3rd 2023at 1:00 pmlunch at 12 pmCampus LimpertsbergB\u00e2timent des Sciences, Room BS 0.03Talk by Professor. Dr. Miguel MarquesMartin Luther University Halle-Wittenberg, GermanyLarge-scale machine-learning assisted discovery and characterization of materialsAbstract:In this talk we discuss our recent attempts to discover, characterize, and understand inorganic compounds using ab initio approaches accelerated by machine learning. We start by motivating why the search for new materials is nowadays one of the most pressing technological problems. Then we summarize our recent work in using crystal-graph attention neural networks for the prediction of materials properties. To train these networks, we curated a dataset of over 2 million density-functional calculations with consistent calculation parameters. Combining the data and the newly developed networks we have already scanned more than two thousand prototypes spanning a space of more than one billion materials and identified tens of thousands of theoretically stable compounds. We then discuss how simple, interpretable machine learning approaches can be used to understand complex material properties, such as the transition temperature of superconductors. Finally, we speculate which role machine learning will have in the future of materials science.About the speaker:Miguel Marques received his PhD degree in Physics from the University of Wuerzburg in 2000, working under the supervision of E.K.U.Gross in the field of density functional theory for superconductors. He then held several post-doctoral positions in Spain, Germany, and in France. From 2005 to 2007 he was assistant professor at the University of Coimbra in Portugal. From 2007 to 2014 he was CNRS researcher at the University of Lyon 1, and from 2014 to 2023 professor at the Martin-Luther University of Halle-Wittenberg. He is now professor at the Research Center Future Energy Materials and Systems of the Ruhr University Bochum. His current research interests include density functional theory, superconductivity, application of machine learning to materials science, etc. He authored more than 200 articles, and has edited three books published by Springer in the Lecture Notes in Physics series. He also organized several summer schools and international workshops, such as the Benasque School and International Workshop in TDDFT, that takes place in Benasque, Spain every second year.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/\" \/>\n<meta property=\"og:site_name\" content=\"FSTM FR\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/fstm.uni.lu\/\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2026\/03\/03111744\/FSTM_SM-Profile_1600x1600px-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"2560\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/\",\"url\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/\",\"name\":\"Physics Colloquium:Large-scale machine-learning assisted discovery and characterization of materials - FSTM I Uni.lu\",\"isPartOf\":{\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2023\/04\/physics_colloquium_large_scale_machine_learning_assisted_discovery_and_characterization_of_materials.jpg\",\"datePublished\":\"2023-04-19T14:17:25+00:00\",\"dateModified\":\"2023-04-19T14:17:25+00:00\",\"description\":\"Physics ColloquiumWednesday, May 3rd 2023at 1:00 pmlunch at 12 pmCampus LimpertsbergB\u00e2timent des Sciences, Room BS 0.03Talk by Professor. Dr. Miguel MarquesMartin Luther University Halle-Wittenberg, GermanyLarge-scale machine-learning assisted discovery and characterization of materialsAbstract:In this talk we discuss our recent attempts to discover, characterize, and understand inorganic compounds using ab initio approaches accelerated by machine learning. We start by motivating why the search for new materials is nowadays one of the most pressing technological problems. Then we summarize our recent work in using crystal-graph attention neural networks for the prediction of materials properties. To train these networks, we curated a dataset of over 2 million density-functional calculations with consistent calculation parameters. Combining the data and the newly developed networks we have already scanned more than two thousand prototypes spanning a space of more than one billion materials and identified tens of thousands of theoretically stable compounds. We then discuss how simple, interpretable machine learning approaches can be used to understand complex material properties, such as the transition temperature of superconductors. Finally, we speculate which role machine learning will have in the future of materials science.About the speaker:Miguel Marques received his PhD degree in Physics from the University of Wuerzburg in 2000, working under the supervision of E.K.U.Gross in the field of density functional theory for superconductors. He then held several post-doctoral positions in Spain, Germany, and in France. From 2005 to 2007 he was assistant professor at the University of Coimbra in Portugal. From 2007 to 2014 he was CNRS researcher at the University of Lyon 1, and from 2014 to 2023 professor at the Martin-Luther University of Halle-Wittenberg. He is now professor at the Research Center Future Energy Materials and Systems of the Ruhr University Bochum. His current research interests include density functional theory, superconductivity, application of machine learning to materials science, etc. He authored more than 200 articles, and has edited three books published by Springer in the Lecture Notes in Physics series. He also organized several summer schools and international workshops, such as the Benasque School and International Workshop in TDDFT, that takes place in Benasque, Spain every second year.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/#primaryimage\",\"url\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2023\/04\/physics_colloquium_large_scale_machine_learning_assisted_discovery_and_characterization_of_materials.jpg\",\"contentUrl\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2023\/04\/physics_colloquium_large_scale_machine_learning_assisted_discovery_and_characterization_of_materials.jpg\",\"width\":870,\"height\":579},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.uni.lu\/fr\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine\",\"item\":\"https:\/\/www.uni.lu\/fstm-fr\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Events\",\"item\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"Physics Colloquium:Large-scale machine-learning assisted discovery and characterization of materials\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/#website\",\"url\":\"https:\/\/www.uni.lu\/fstm-fr\/\",\"name\":\"FSTM\",\"description\":\"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine I Uni.lu\",\"publisher\":{\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/#organization\"},\"alternateName\":\"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine I Universit\u00e9 du Luxembourg\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.uni.lu\/fstm-fr\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/#organization\",\"name\":\"FSTM - Universit\u00e9 du Luxembourg I Uni.lu\",\"alternateName\":\"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine\",\"url\":\"https:\/\/www.uni.lu\/fstm-fr\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2026\/03\/03111744\/FSTM_SM-Profile_1600x1600px-scaled.jpg\",\"contentUrl\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2026\/03\/03111744\/FSTM_SM-Profile_1600x1600px-scaled.jpg\",\"width\":2560,\"height\":2560,\"caption\":\"FSTM - Universit\u00e9 du Luxembourg I Uni.lu\"},\"image\":{\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/fstm.uni.lu\/\",\"https:\/\/www.linkedin.com\/showcase\/fstm-uni-lu\"]}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Physics Colloquium:Large-scale machine-learning assisted discovery and characterization of materials - FSTM I Uni.lu","description":"Physics ColloquiumWednesday, May 3rd 2023at 1:00 pmlunch at 12 pmCampus LimpertsbergB\u00e2timent des Sciences, Room BS 0.03Talk by Professor. Dr. Miguel MarquesMartin Luther University Halle-Wittenberg, GermanyLarge-scale machine-learning assisted discovery and characterization of materialsAbstract:In this talk we discuss our recent attempts to discover, characterize, and understand inorganic compounds using ab initio approaches accelerated by machine learning. We start by motivating why the search for new materials is nowadays one of the most pressing technological problems. Then we summarize our recent work in using crystal-graph attention neural networks for the prediction of materials properties. To train these networks, we curated a dataset of over 2 million density-functional calculations with consistent calculation parameters. Combining the data and the newly developed networks we have already scanned more than two thousand prototypes spanning a space of more than one billion materials and identified tens of thousands of theoretically stable compounds. We then discuss how simple, interpretable machine learning approaches can be used to understand complex material properties, such as the transition temperature of superconductors. Finally, we speculate which role machine learning will have in the future of materials science.About the speaker:Miguel Marques received his PhD degree in Physics from the University of Wuerzburg in 2000, working under the supervision of E.K.U.Gross in the field of density functional theory for superconductors. He then held several post-doctoral positions in Spain, Germany, and in France. From 2005 to 2007 he was assistant professor at the University of Coimbra in Portugal. From 2007 to 2014 he was CNRS researcher at the University of Lyon 1, and from 2014 to 2023 professor at the Martin-Luther University of Halle-Wittenberg. He is now professor at the Research Center Future Energy Materials and Systems of the Ruhr University Bochum. His current research interests include density functional theory, superconductivity, application of machine learning to materials science, etc. He authored more than 200 articles, and has edited three books published by Springer in the Lecture Notes in Physics series. He also organized several summer schools and international workshops, such as the Benasque School and International Workshop in TDDFT, that takes place in Benasque, Spain every second year.","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\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/","og_locale":"fr_FR","og_type":"article","og_title":"Physics Colloquium:Large-scale machine-learning assisted discovery and characterization of materials","og_description":"Physics ColloquiumWednesday, May 3rd 2023at 1:00 pmlunch at 12 pmCampus LimpertsbergB\u00e2timent des Sciences, Room BS 0.03Talk by Professor. Dr. Miguel MarquesMartin Luther University Halle-Wittenberg, GermanyLarge-scale machine-learning assisted discovery and characterization of materialsAbstract:In this talk we discuss our recent attempts to discover, characterize, and understand inorganic compounds using ab initio approaches accelerated by machine learning. We start by motivating why the search for new materials is nowadays one of the most pressing technological problems. Then we summarize our recent work in using crystal-graph attention neural networks for the prediction of materials properties. To train these networks, we curated a dataset of over 2 million density-functional calculations with consistent calculation parameters. Combining the data and the newly developed networks we have already scanned more than two thousand prototypes spanning a space of more than one billion materials and identified tens of thousands of theoretically stable compounds. We then discuss how simple, interpretable machine learning approaches can be used to understand complex material properties, such as the transition temperature of superconductors. Finally, we speculate which role machine learning will have in the future of materials science.About the speaker:Miguel Marques received his PhD degree in Physics from the University of Wuerzburg in 2000, working under the supervision of E.K.U.Gross in the field of density functional theory for superconductors. He then held several post-doctoral positions in Spain, Germany, and in France. From 2005 to 2007 he was assistant professor at the University of Coimbra in Portugal. From 2007 to 2014 he was CNRS researcher at the University of Lyon 1, and from 2014 to 2023 professor at the Martin-Luther University of Halle-Wittenberg. He is now professor at the Research Center Future Energy Materials and Systems of the Ruhr University Bochum. His current research interests include density functional theory, superconductivity, application of machine learning to materials science, etc. He authored more than 200 articles, and has edited three books published by Springer in the Lecture Notes in Physics series. He also organized several summer schools and international workshops, such as the Benasque School and International Workshop in TDDFT, that takes place in Benasque, Spain every second year.","og_url":"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/","og_site_name":"FSTM FR","article_publisher":"https:\/\/www.facebook.com\/fstm.uni.lu\/","og_image":[{"width":2560,"height":2560,"url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2026\/03\/03111744\/FSTM_SM-Profile_1600x1600px-scaled.jpg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/","url":"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/","name":"Physics Colloquium:Large-scale machine-learning assisted discovery and characterization of materials - FSTM I Uni.lu","isPartOf":{"@id":"https:\/\/www.uni.lu\/fstm-fr\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/#primaryimage"},"image":{"@id":"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/#primaryimage"},"thumbnailUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2023\/04\/physics_colloquium_large_scale_machine_learning_assisted_discovery_and_characterization_of_materials.jpg","datePublished":"2023-04-19T14:17:25+00:00","dateModified":"2023-04-19T14:17:25+00:00","description":"Physics ColloquiumWednesday, May 3rd 2023at 1:00 pmlunch at 12 pmCampus LimpertsbergB\u00e2timent des Sciences, Room BS 0.03Talk by Professor. Dr. Miguel MarquesMartin Luther University Halle-Wittenberg, GermanyLarge-scale machine-learning assisted discovery and characterization of materialsAbstract:In this talk we discuss our recent attempts to discover, characterize, and understand inorganic compounds using ab initio approaches accelerated by machine learning. We start by motivating why the search for new materials is nowadays one of the most pressing technological problems. Then we summarize our recent work in using crystal-graph attention neural networks for the prediction of materials properties. To train these networks, we curated a dataset of over 2 million density-functional calculations with consistent calculation parameters. Combining the data and the newly developed networks we have already scanned more than two thousand prototypes spanning a space of more than one billion materials and identified tens of thousands of theoretically stable compounds. We then discuss how simple, interpretable machine learning approaches can be used to understand complex material properties, such as the transition temperature of superconductors. Finally, we speculate which role machine learning will have in the future of materials science.About the speaker:Miguel Marques received his PhD degree in Physics from the University of Wuerzburg in 2000, working under the supervision of E.K.U.Gross in the field of density functional theory for superconductors. He then held several post-doctoral positions in Spain, Germany, and in France. From 2005 to 2007 he was assistant professor at the University of Coimbra in Portugal. From 2007 to 2014 he was CNRS researcher at the University of Lyon 1, and from 2014 to 2023 professor at the Martin-Luther University of Halle-Wittenberg. He is now professor at the Research Center Future Energy Materials and Systems of the Ruhr University Bochum. His current research interests include density functional theory, superconductivity, application of machine learning to materials science, etc. He authored more than 200 articles, and has edited three books published by Springer in the Lecture Notes in Physics series. He also organized several summer schools and international workshops, such as the Benasque School and International Workshop in TDDFT, that takes place in Benasque, Spain every second year.","breadcrumb":{"@id":"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/"]}]},{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/#primaryimage","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2023\/04\/physics_colloquium_large_scale_machine_learning_assisted_discovery_and_characterization_of_materials.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2023\/04\/physics_colloquium_large_scale_machine_learning_assisted_discovery_and_characterization_of_materials.jpg","width":870,"height":579},{"@type":"BreadcrumbList","@id":"https:\/\/www.uni.lu\/fstm-fr\/events\/physics-colloquiumlarge-scale-machine-learning-assisted-discovery-and-characterization-of-materials\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.uni.lu\/fr"},{"@type":"ListItem","position":2,"name":"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine","item":"https:\/\/www.uni.lu\/fstm-fr\/"},{"@type":"ListItem","position":3,"name":"Events","item":"https:\/\/www.uni.lu\/fstm-fr\/events\/"},{"@type":"ListItem","position":4,"name":"Physics Colloquium:Large-scale machine-learning assisted discovery and characterization of materials"}]},{"@type":"WebSite","@id":"https:\/\/www.uni.lu\/fstm-fr\/#website","url":"https:\/\/www.uni.lu\/fstm-fr\/","name":"FSTM","description":"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine I Uni.lu","publisher":{"@id":"https:\/\/www.uni.lu\/fstm-fr\/#organization"},"alternateName":"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine I Universit\u00e9 du Luxembourg","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.uni.lu\/fstm-fr\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"fr-FR"},{"@type":"Organization","@id":"https:\/\/www.uni.lu\/fstm-fr\/#organization","name":"FSTM - Universit\u00e9 du Luxembourg I Uni.lu","alternateName":"Facult\u00e9 des Sciences, des Technologies et de M\u00e9decine","url":"https:\/\/www.uni.lu\/fstm-fr\/","logo":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fstm-fr\/#\/schema\/logo\/image\/","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2026\/03\/03111744\/FSTM_SM-Profile_1600x1600px-scaled.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2026\/03\/03111744\/FSTM_SM-Profile_1600x1600px-scaled.jpg","width":2560,"height":2560,"caption":"FSTM - Universit\u00e9 du Luxembourg I Uni.lu"},"image":{"@id":"https:\/\/www.uni.lu\/fstm-fr\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/fstm.uni.lu\/","https:\/\/www.linkedin.com\/showcase\/fstm-uni-lu"]}]}},"_links":{"self":[{"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events\/2277"}],"collection":[{"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events"}],"about":[{"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/types\/events"}],"replies":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/comments?post=2277"}],"version-history":[{"count":0,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events\/2277\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/media\/2278"}],"wp:attachment":[{"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/media?parent=2277"}],"wp:term":[{"taxonomy":"events-topic","embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events-topic?post=2277"},{"taxonomy":"events-type","embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/events-type?post=2277"},{"taxonomy":"organisation","embeddable":true,"href":"https:\/\/www.uni.lu\/fstm-fr\/wp-json\/wp\/v2\/organisation?post=2277"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}