{"id":27836,"date":"2025-08-30T17:55:37","date_gmt":"2025-08-30T12:10:37","guid":{"rendered":"https:\/\/www.revoscience.com\/en\/?p=27836"},"modified":"2025-08-30T17:55:40","modified_gmt":"2025-08-30T12:10:40","slug":"tohoku-university-researchers-develop-ai-powered-map-to-accelerate-materials-discovery","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/tohoku-university-researchers-develop-ai-powered-map-to-accelerate-materials-discovery\/","title":{"rendered":"Tohoku University Researchers Develop AI-Powered Map to Accelerate Materials Discovery"},"content":{"rendered":"\n
\"\"
Data\u2011analysis workflow. Experimental and computational datasets are unified; crystal\u2011structure graphs, deep learning, and dimensionality reduction yield the materials map.<\/sup><\/em><\/figcaption><\/figure>\n\n\n\n

Researchers at Tohoku University have developed a groundbreaking tool that combines experimental and computational data into a single, intelligent resource: a materials map that instantly guides scientists to the most promising materials from a vast pool of candidates.<\/p>\n\n\n\n

Identifying the right material remains one of the biggest challenges in materials science. While theoretical predictions and experimental validations both play vital roles, they\u2019ve traditionally progressed on separate paths. <\/p>\n\n\n\n

Now, a team at Tohoku University has bridged that divide by developing an AI-generated map that integrates experimental data from scientific literature with computational data derived from first-principles simulations.<\/p>\n\n\n\n

This innovative map acts as a graphical interface, plotting materials based on their thermoelectric performance (zT) and structural similarity. Each point on the map represents a material, and those with similar structures appear close together. <\/p>\n\n\n\n

Since structurally analogous materials are often synthesized and tested using similar techniques, the map allows researchers to quickly identify high-performance analogs and adapt existing synthesis methods\u2014dramatically reducing trial-and-error.<\/p>\n\n\n\n

The project was led by Specially Appointed Associate Professor Yusuke Hashimoto and Professor Takaaki Tomai from FRIS, in collaboration with Assistant Professor Xue Jia and Professor Hao Li from WPI-AIMR. Their approach builds on a previously integrated dataset combining StarryData2 literature entries with computational data from the Materials Project. This dataset was used to train MatDeepLearn (MDL), enhanced with a message passing neural network (MPNN) to predict thermoelectric properties.<\/p>\n\n\n\n

Hashimoto explains, \u201cBy offering an intuitive, bird\u2019s-eye view of countless material candidates, the map enables researchers to identify promising options at a glance. We expect this will significantly shorten the development timeline for new functional materials.\u201d<\/p>\n\n\n\n

This AI-powered map marks a major step forward in streamlining materials discovery, offering scientists a smarter, faster way to navigate the complex landscape of material selection.<\/p>\n","protected":false},"excerpt":{"rendered":"

Researchers at Tohoku University have developed a groundbreaking tool that combines experimental and computational data into a single, intelligent resource: a materials map that instantly guides scientists to the most promising materials from a vast pool of candidates.<\/p>\n","protected":false},"author":2,"featured_media":27837,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17],"tags":[],"class_list":["post-27836","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3.webp",1100,619,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3-200x200.webp",200,200,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3-675x380.webp",675,380,true],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3-768x432.webp",750,422,true],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3.webp",750,422,false],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3.webp",1100,619,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3.webp",1100,619,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3.webp",1100,619,false],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3-870x570.webp",870,570,true],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3-600x619.webp",600,619,true],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3-600x600.webp",600,600,true],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3-760x490.webp",760,490,true],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3-550x360.webp",550,360,true],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3-95x65.webp",95,65,true],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3-640x619.webp",640,619,true],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3-96x96.webp",96,96,true],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/08\/image-3-150x84.webp",150,84,true]},"author_info":{"info":["RevoScience"]},"category_info":"Research<\/a>","tag_info":"Research","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/27836","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/comments?post=27836"}],"version-history":[{"count":1,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/27836\/revisions"}],"predecessor-version":[{"id":27838,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/27836\/revisions\/27838"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/27837"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=27836"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=27836"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=27836"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}