{"id":201561,"date":"2024-12-13T22:04:21","date_gmt":"2024-12-14T04:04:21","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2024\/12\/ai-at-scale-method-accelerates-atomistic-simulations-for-scientists"},"modified":"2024-12-13T22:04:21","modified_gmt":"2024-12-14T04:04:21","slug":"ai-at-scale-method-accelerates-atomistic-simulations-for-scientists","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2024\/12\/ai-at-scale-method-accelerates-atomistic-simulations-for-scientists","title":{"rendered":"\u2018AI-at-scale\u2019 method accelerates atomistic simulations for scientists"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/ai-at-scale-method-accelerates-atomistic-simulations-for-scientists3.jpg\"><\/a><\/p>\n<p>Quantum calculations of molecular systems often require extraordinary amounts of computing power; these calculations are typically performed on the world\u2019s largest supercomputers to better understand real-world products such as batteries and semiconductors.<\/p>\n<p>Now, UC Berkeley and Lawrence Berkeley National Laboratory (Berkeley Lab) researchers have developed a new machine learning method that significantly speeds up <a href=\"https:\/\/techxplore.com\/tags\/atomistic+simulations\/\" rel=\"tag\" class=\"\">atomistic simulations<\/a> by improving model scalability. This approach reduces the computing memory required for simulations by more than fivefold compared to existing models and delivers results over ten times faster.<\/p>\n<p>Their research has been accepted at <a href=\"https:\/\/neurips.cc\/\" target=\"_blank\">Neural Information Processing Systems (NeurIPS) 2024<\/a>, a conference and publication venue in artificial intelligence and machine learning. They will present their work at the conference on <a href=\"https:\/\/neurips.cc\/virtual\/2024\/poster\/94722\" target=\"_blank\">December 13<\/a>, and a version of their paper is <a href=\"https:\/\/arxiv.org\/abs\/2410.24169\" target=\"_blank\">available<\/a> on the <i>arXiv<\/i> preprint server.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Quantum calculations of molecular systems often require extraordinary amounts of computing power; these calculations are typically performed on the world\u2019s largest supercomputers to better understand real-world products such as batteries and semiconductors. Now, UC Berkeley and Lawrence Berkeley National Laboratory (Berkeley Lab) researchers have developed a new machine learning method that significantly speeds up atomistic [\u2026]<\/p>\n","protected":false},"author":662,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1617,6,44],"tags":[],"class_list":["post-201561","post","type-post","status-publish","format-standard","hentry","category-quantum-physics","category-robotics-ai","category-supercomputing"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/201561","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/662"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=201561"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/201561\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=201561"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=201561"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=201561"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}