{"id":240984,"date":"2026-07-16T00:47:48","date_gmt":"2026-07-16T05:47:48","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2026\/07\/with-machine-learning-researchers-embrace-the-atomic-scale-complexity-of-batteries"},"modified":"2026-07-16T00:47:48","modified_gmt":"2026-07-16T05:47:48","slug":"with-machine-learning-researchers-embrace-the-atomic-scale-complexity-of-batteries","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2026\/07\/with-machine-learning-researchers-embrace-the-atomic-scale-complexity-of-batteries","title":{"rendered":"With machine learning, researchers embrace the atomic-scale complexity of batteries"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/with-machine-learning-researchers-embrace-the-atomic-scale-complexity-of-batteries.jpg\"><\/a><\/p>\n<p>For grid-scale energy storage and national energy resilience, the U.S. needs better batteries. Lawrence Livermore National Laboratory (LLNL) scientists are tackling that challenge in many ways, but one approach is making a significant impact: physics-informed machine learning.<\/p>\n<p>In two recent publications, LLNL researchers examined how integrating molecular dynamics simulations with physics-informed machine learning can illuminate the relationships between structure and behavior in complex battery materials. They used the combination of techniques to explore carbon anodes in sodium-ion batteries and liquid electrolytes in lithium-ion batteries.<\/p>\n<p>\u201cThese studies show that the structural complexity of battery materials is not just an obstacle to understanding but a design advantage, laying the groundwork for high-throughput screening of next-generation energy-storage materials,\u201d said LLNL scientist and author Liwen (Sabrina) Wan. \u201cBy encoding that complexity into physics-informed machine learning models, we can predict properties and identify design levers that traditional approaches simply cannot access.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>For grid-scale energy storage and national energy resilience, the U.S. needs better batteries. Lawrence Livermore National Laboratory (LLNL) scientists are tackling that challenge in many ways, but one approach is making a significant impact: physics-informed machine learning. In two recent publications, LLNL researchers examined how integrating molecular dynamics simulations with physics-informed machine learning can illuminate [\u2026]<\/p>\n","protected":false},"author":662,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1635,6],"tags":[],"class_list":["post-240984","post","type-post","status-publish","format-standard","hentry","category-materials","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/240984","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=240984"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/240984\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=240984"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=240984"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=240984"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}