{"id":230591,"date":"2026-02-05T05:30:55","date_gmt":"2026-02-05T11:30:55","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2026\/02\/dive-multi-agent-workflow-streamlines-hydrogen-storage-materials-discovery"},"modified":"2026-02-05T05:30:55","modified_gmt":"2026-02-05T11:30:55","slug":"dive-multi-agent-workflow-streamlines-hydrogen-storage-materials-discovery","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2026\/02\/dive-multi-agent-workflow-streamlines-hydrogen-storage-materials-discovery","title":{"rendered":"DIVE multi-agent workflow streamlines hydrogen storage materials discovery"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/dive-multi-agent-workflow-streamlines-hydrogen-storage-materials-discovery2.jpg\"><\/a><\/p>\n<p>Developing new materials can involve a dizzying amount of trial and error for different configurations and elements. Artificial intelligence (AI) has seen a surge of popularity in energy materials research for its potential to streamline this time-consuming process. However, fully autonomous workflows that connect high-precision experimental knowledge to the discovery of credible new energy-related materials remain at an early stage.<\/p>\n<p>A team of researchers at the WPI-Advanced Institute for Materials Research (WPI-AIMR), Tohoku University, created the Descriptive Interpretation of Visual Expression (DIVE) multi-agent workflow to streamline the material research process. The system extracts information from images in a database of over 30,000 entries from 4,000 scientific publications to propose new materials within minutes.<\/p>\n<p><a href=\"https:\/\/pubs.rsc.org\/en\/content\/articlelanding\/2026\/sc\/d5sc09921h\" target=\"_blank\">The findings<\/a> were published in Chemical Science.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Developing new materials can involve a dizzying amount of trial and error for different configurations and elements. Artificial intelligence (AI) has seen a surge of popularity in energy materials research for its potential to streamline this time-consuming process. However, fully autonomous workflows that connect high-precision experimental knowledge to the discovery of credible new energy-related materials [\u2026]<\/p>\n","protected":false},"author":427,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19,6],"tags":[],"class_list":["post-230591","post","type-post","status-publish","format-standard","hentry","category-chemistry","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/230591","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\/427"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=230591"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/230591\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=230591"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=230591"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=230591"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}