{"id":48470,"date":"2023-10-16T10:14:35","date_gmt":"2023-10-16T08:14:35","guid":{"rendered":"https:\/\/www.h-its.org\/de\/event\/hits-simplaix-joint-colloquium-volker-deringer-machine-learning-driven-advances-in-modelling-and-understanding-disordered-materials\/"},"modified":"2026-01-26T11:16:43","modified_gmt":"2026-01-26T10:16:43","slug":"hits-simplaix-joint-colloquium-volker-deringer","status":"publish","type":"tribe_events","link":"https:\/\/www.h-its.org\/de\/event\/hits-simplaix-joint-colloquium-volker-deringer\/","title":{"rendered":"HITS-SIMPLAIX Joint Colloquium Volker Deringer: Machine-learning-driven advances in modelling and understanding disordered materials"},"content":{"rendered":"<p>&nbsp;<\/p>\n<p>By <a href=\"https:\/\/www.chem.ox.ac.uk\/people\/volker-deringer\">Volker Deringer<\/a>, Department of Chemistry, University of Oxford, UK<\/p>\n<p>Machine learning (ML) based interatomic potential models are increasingly popular simulation tools for molecular and materials systems and hold promise for use on exascale supercomputers [1]. ML potentials are fitted to large sets of quantum-mechanical reference data, and therefore developing high-quality datasets and automated training approaches is becoming an increasingly important re-search challenge. In this seminar, I will highlight some recent developments in ML-driven molecular-dynamics simulations of structurally complex inorganic materials, combining methodological aspects and practical applications. In regard to methods, I will discuss the use of cheaply available \u201csynthetic\u201d data in pre-training atomistic ML models [2], which can improve accuracy and robustness of neural-network interatomic potentials compared to direct training on quantum-mechanical data [3]. Regarding applications, I will showcase device-scale simulations of phase-change memory materials (which encode digital \u201cones\u201d and \u201czeroes\u201d in data-storage devices) [4]. Finally, I will discuss perspectives for the development of both purpose-specific and generally applicable ML potentials for materials.<\/p>\n<p>[1] C. Chang et al., Nat. Rev. Mater. 8, 309 (2023).<br \/>\n[2] J. L. A. Gardner et al., Digital Discovery 2, 651 (2023).<br \/>\n[3] J. L. A. Gardner et al., arXiv:2307.15714 [physics.comp-ph].<br \/>\n[4] Y. Zhou et al., Nat. Electron., DOI: 10.1038\/s41928-023-01030-x (2023).<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p><strong>REGISTRATION:<\/strong><\/p>\n<article>The talk will be hybrid.<\/article>\n<article>If you would like to participate in person, please register in advance at <a href=\"mailto:benedicta.frech@h-its.org\">benedicta.frech@h-its.org<\/a>.<\/article>\n<article>If you would like to participate online, please use the following link: <a href=\"https:\/\/kta-email.zoom.us\/meeting\/register\/tJIqcuyrpzIoH9KZWyFkzZoddoPQdtZDml5j\">https:\/\/kta-email.zoom.us\/meeting\/register\/tJIqcuyrpzIoH9KZWyFkzZoddoPQdtZDml5j<\/a><\/article>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp; By Volker Deringer, Department of Chemistry, University of Oxford, UK Machine learning (ML) based interatomic potential models are increasingly popular &#8230;<\/p>\n","protected":false},"author":42,"featured_media":48472,"template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"hits-research-group":[1319,1422],"tribe_events_cat":[1345,1408],"class_list":["post-48470","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","hits-research-group-hits","hits-research-group-simplaix","tribe_events_cat-oeffentliche-veranstaltungen","tribe_events_cat-kolloquien","cat_oeffentliche-veranstaltungen","cat_kolloquien"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>HITS-SIMPLAIX Joint Colloquium Volker Deringer: Machine-learning-driven advances in modelling and understanding disordered materials - HITS gGmbH<\/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:\/\/www.h-its.org\/de\/event\/hits-simplaix-joint-colloquium-volker-deringer\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"HITS-SIMPLAIX Joint Colloquium Volker Deringer: Machine-learning-driven advances in modelling and understanding disordered materials - HITS gGmbH\" \/>\n<meta property=\"og:description\" content=\"&nbsp; 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