{"id":10776,"date":"2026-10-16T12:54:26","date_gmt":"2026-10-16T10:54:26","guid":{"rendered":"https:\/\/hch2.de\/?post_type=aktuelles&p=10776"},"modified":"2026-10-16T12:54:27","modified_gmt":"2026-10-16T10:54:27","slug":"the-needle-in-the-data-haystack-how-ai-could-speed-up-research","status":"publish","type":"aktuelles","link":"https:\/\/hch2.de\/en\/news\/the-needle-in-the-data-haystack-how-ai-could-speed-up-research\/","title":{"rendered":"The needle in the data haystack: how AI could speed up research"},"content":{"rendered":"\n
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Large-scale research facilities generate enormous volumes of data. And those volumes keep growing. Prof. <\/a>Hans-Georg Steinr\u00fcck<\/a>, a physicist at Forschungszentrum J\u00fclich<\/a>, describes this as a \u201chistoric transformation\u201d. To make more effective use of these vast quantities of data, eight research institutions are working together to use artificial intelligence to find relevant results in datasets more quickly. Their joint project is called Physics-LLM. Among those involved are Hans-Georg Steinr\u00fcck and his team at J\u00fclich\u2019s Institute for a sustainable Hydrogen Economy (IHE)<\/a>. <\/strong><\/p>\n\n\n\n

At IHE, for example, researchers generate large volumes of data by investigating the chemical reactions involved in hydrogen storage down to the atomic level. During experiments at synchrotrons<\/a> or free-electron lasers<\/a>, they record up to several thousand images per second. Yet even long-term studies in IHE\u2019s laboratories, capturing just one image per second over weeks or months, can produce enormous datasets. These data provide valuable insights into chemical reactions and reaction pathways, as well as catalyst ageing, loss of performance and regeneration. The crucial information is often hidden like the proverbial needle in a haystack. Physics-LLM aims to help researchers pinpoint this information, analyse large datasets efficiently and identify connections. It will also support them in preparing experiments and writing the code needed to run them.<\/p>\n\n\n\n

For Hans-Georg Steinr\u00fcck, these opportunities also raise a central question: how can researchers retain control over AI-assisted research? They should not simply let AI take charge. Their own experimentation will remain essential.<\/p>\n\n\n\n

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Professor Hans-Georg Steinr\u00fcck is one of the researchers working on Physics-LLM. Foto: Forschungszentrum J\u00fclich\/Steinr\u00fcck<\/figcaption><\/figure>\n<\/blockquote>\n\n\n\n
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The Federal Ministry of Research, Technology and Space<\/a> is providing around \u20ac2.9 million in funding until October 2028 for Physics-LLM: Applying Large Language Models to Research Data Management in Physics.<\/p>\n\n\n\n

Alongside Forschungszentrum J\u00fclich, the partners in Physics-LLM are Helmholtz-Zentrum Hereon<\/a>, the University of Bonn<\/a>, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg<\/a>, the Leibniz Institute for Astrophysics Potsdam<\/a>, Deutsches Elektronen-Synchrotron DESY<\/a>, TU Dortmund University<\/a> and the Technical University of Munich.<\/a><\/p>\n\n\n\n

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The copyright for the images used on this website is held by Forschungszentrum J\u00fclich, aligator kommunikation GmbH and stock.adobe.com.<\/p>\n","protected":false},"featured_media":10773,"template":"","categories":[253,252,254],"tags":[263,279],"class_list":["post-10776","aktuelles","type-aktuelles","status-publish","has-post-thumbnail","hentry","category-forschung-en","category-general-en","category-wasserstoff-en","tag-science","tag-science-en"],"yoast_head":"\nThe needle in the data haystack: how AI could speed up research - HCH2<\/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:\/\/hch2.de\/en\/news\/the-needle-in-the-data-haystack-how-ai-could-speed-up-research\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The needle in the data haystack: how AI could speed up research - HCH2\" \/>\n<meta property=\"og:description\" content=\"Large-scale research facilities generate enormous volumes of data. 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