{"id":7538,"date":"2019-02-05T17:14:16","date_gmt":"2019-02-05T16:14:16","guid":{"rendered":"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/"},"modified":"2019-02-05T17:14:16","modified_gmt":"2019-02-05T16:14:16","slug":"from-machine-learning-interatomic-potentials-to-materials-chemistry","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/","title":{"rendered":"From Machine-Learning Interatomic Potentials to Materials Chemistry"},"content":{"rendered":"
\n

\nAbout the topic<\/h2>\n

Understanding the links between structure and properties in materials is a formidable task. Atomic-scale simulations based on density-functional theory (DFT) have played important roles in this \u2013 but they are computationally expensive and can describe complex materials only in small model systems. Novel interatomic potentials based on machine learning (ML) have recently garnered a lot of attention in computational physics, chemistry, and materials science: these simulation tools achieve close-to DFT accuracy at only a fraction of the cost.<\/p>

In the first part of this talk, I will argue that ML-based interatomic potentials are particularly useful for studying materials with complex structures, such as amorphous (non-crystalline) solids. I will describe an ML potential for amorphous carbon [1] that was built using the Gaussian Approximation Potential (GAP) framework [2], with a special view on what is needed to create and validate ML potentials for the amorphous state. I will present recent applications to porous and partly \u00ab\u00a0graphitised\u00a0\u00bb carbons that are relevant for batteries and supercapacitors [3], and to amorphous silicon, where ML-driven simulations allowed us to unlock long simulation times and accurate atomistic structures [4].<\/p>

In the second part, I will point out possible directions for the automated exploration and \u201clearning\u201d of condensed-phase potential-energy landscapes. We have recently introduced an ML-driven approach to inorganic crystal structure prediction, dubbed GAP-driven random structure searching (GAP-RSS) [5]. This technique, iteratively exploring and fitting structural space, allowed us to create to a flexible and accurate interatomic potential model for elemental boron [5]. We later showed how GAP-RSS can \u201cdiscover\u201d phosphorus allotropes without prior structural knowledge [6]. These early results promise further applications of ML-driven simulation methods in materials chemistry.<\/p>

[1] V. L. Deringer, G. Cs\u00e1nyi, Phys. Rev. B 95, 094203 (2017).<\/p>

[2] A. P. Bart\u00f3k, M. C. Payne, R. Kondor, G. Cs\u00e1nyi, Phys. Rev. Lett. 104, 136403 (2010).<\/p>

[3] V. L. Deringer, C. Merlet, Y. Hu, et al., Chem. Commun. 54, 5988 (2018).<\/p>

[4] V. L. Deringer, N. Bernstein, A. P. Bart\u00f3k, et al., J. Phys. Chem. Lett. 9, 2879 (2018).<\/p>

[5] V. L. Deringer, C. J. Pickard, G. Cs\u00e1nyi, Phys. Rev. Lett. 120, 156001 (2018).<\/p>

[6] V. L. Deringer, D. M. Proserpio, G. Cs\u00e1nyi, C. J. Pickard, Faraday Discuss. 211, 45 (2018).<\/p>\n

\nAbout the speaker<\/h2>\n

Volker Deringer<\/a> studied chemistry at RWTH Aachen University (Germany) where he received his diploma (2010) and doctorate (2014) under guidance of Richard Dronskowski. In 2015, he moved to the University of Cambridge (UK), where he held a Feodor Lynen Fellowship from the Alexander von Humboldt Foundation (2015\u20132017, hosted by G\u00e1bor Cs\u00e1nyi) and was awarded a Leverhulme Early Career Fellowship in 2017. His research combines quantum-mechanical simulation methods with machine learning to understand the connections between structure, bonding, and properties in complex materials.<\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"","protected":false},"author":0,"featured_media":7539,"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":"2019-02-27 11:00:00","event_end_date":"2019-02-27 12:30:00","event_speaker_name":"Dr. Volker Deringer, University of Cambridge","event_speaker_link":"","event_is_online":false,"event_location":"Campus Limpertsberg - B\u00e2timent des Sciences","event_street":"","event_location_link":"","event_zip_code":"","event_city":"","event_country":"LU"},"events-topic":[316],"events-type":[],"organisation":[76,25,226],"authorship":[],"acf":[],"yoast_head":"\nFrom Machine-Learning Interatomic Potentials to Materials Chemistry - Universit\u00e9 du Luxembourg<\/title>\n<meta name=\"description\" content=\"Understanding the links between structure and properties in materials is a formidable task. Atomic-scale simulations based on density-functional theory\" \/>\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\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"From Machine-Learning Interatomic Potentials to Materials Chemistry\" \/>\n<meta property=\"og:description\" content=\"Understanding the links between structure and properties in materials is a formidable task. Atomic-scale simulations based on density-functional theory\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/\" \/>\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\/2026\/03\/03120045\/UNIV_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<meta name=\"twitter:label1\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/\",\"url\":\"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/\",\"name\":\"From Machine-Learning Interatomic Potentials to Materials Chemistry - Universit\u00e9 du Luxembourg\",\"isPartOf\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2019\/02\/from_machine_learning_interatomic_potentials_to_materials_chemistry.jpg\",\"datePublished\":\"2019-02-05T16:14:16+00:00\",\"dateModified\":\"2019-02-05T16:14:16+00:00\",\"description\":\"Understanding the links between structure and properties in materials is a formidable task. Atomic-scale simulations based on density-functional theory\",\"breadcrumb\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/#primaryimage\",\"url\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2019\/02\/from_machine_learning_interatomic_potentials_to_materials_chemistry.jpg\",\"contentUrl\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2019\/02\/from_machine_learning_interatomic_potentials_to_materials_chemistry.jpg\",\"width\":800,\"height\":600},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.uni.lu\/fr\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Events\",\"item\":\"https:\/\/www.uni.lu\/fr\/events\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"From Machine-Learning Interatomic Potentials to Materials Chemistry\"}]},{\"@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":"From Machine-Learning Interatomic Potentials to Materials Chemistry - Universit\u00e9 du Luxembourg","description":"Understanding the links between structure and properties in materials is a formidable task. Atomic-scale simulations based on density-functional theory","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\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/","og_locale":"fr_FR","og_type":"article","og_title":"From Machine-Learning Interatomic Potentials to Materials Chemistry","og_description":"Understanding the links between structure and properties in materials is a formidable task. Atomic-scale simulations based on density-functional theory","og_url":"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/","og_site_name":"UNI FR","article_publisher":"https:\/\/www.facebook.com\/uni.lu","og_image":[{"width":2560,"height":2560,"url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","twitter_misc":{"Dur\u00e9e de lecture estim\u00e9e":"2 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/","url":"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/","name":"From Machine-Learning Interatomic Potentials to Materials Chemistry - Universit\u00e9 du Luxembourg","isPartOf":{"@id":"https:\/\/www.uni.lu\/fr\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/#primaryimage"},"image":{"@id":"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/#primaryimage"},"thumbnailUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2019\/02\/from_machine_learning_interatomic_potentials_to_materials_chemistry.jpg","datePublished":"2019-02-05T16:14:16+00:00","dateModified":"2019-02-05T16:14:16+00:00","description":"Understanding the links between structure and properties in materials is a formidable task. Atomic-scale simulations based on density-functional theory","breadcrumb":{"@id":"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/"]}]},{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/#primaryimage","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2019\/02\/from_machine_learning_interatomic_potentials_to_materials_chemistry.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2019\/02\/from_machine_learning_interatomic_potentials_to_materials_chemistry.jpg","width":800,"height":600},{"@type":"BreadcrumbList","@id":"https:\/\/www.uni.lu\/fr\/events\/from-machine-learning-interatomic-potentials-to-materials-chemistry\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.uni.lu\/fr\/"},{"@type":"ListItem","position":2,"name":"Events","item":"https:\/\/www.uni.lu\/fr\/events\/"},{"@type":"ListItem","position":3,"name":"From Machine-Learning Interatomic Potentials to Materials Chemistry"}]},{"@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"]}]}},"_links":{"self":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events\/7538"}],"collection":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events"}],"about":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/types\/events"}],"replies":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/comments?post=7538"}],"version-history":[{"count":0,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events\/7538\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/media\/7539"}],"wp:attachment":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/media?parent=7538"}],"wp:term":[{"taxonomy":"events-topic","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events-topic?post=7538"},{"taxonomy":"events-type","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events-type?post=7538"},{"taxonomy":"organisation","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/organisation?post=7538"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}