{"id":38359,"date":"2026-07-01T15:44:12","date_gmt":"2026-07-01T09:59:12","guid":{"rendered":"https:\/\/www.revoscience.com\/en\/?p=38359"},"modified":"2026-07-01T15:44:14","modified_gmt":"2026-07-01T09:59:14","slug":"interpretable-ai-in-materials-discovery-uncovering-how-models-make-predictions","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/interpretable-ai-in-materials-discovery-uncovering-how-models-make-predictions\/","title":{"rendered":"Interpretable AI in materials discovery: uncovering how models make predictions"},"content":{"rendered":"\n
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In recent years, artificial intelligence (AI) has emerged as a powerful tool to predict how materials will behave based on their atomic structure, helping researchers discover new materials faster and reduce reliance on trial-and-error methods. <\/p>\n\n\n\n

However, many of these models work like “black boxes.” They can make accurate predictions, but they do not explain how those predictions are made. This makes it difficult to understand the relationships between a material\u2019s structure and its properties, limiting how useful these models are for guiding the development of new designs.<\/p>\n\n\n\n

Now, in a study to be published in the journal\u00a0Advanced Intelligent Discovery<\/em><\/a>\u00a0on June 15, 2026, researchers from the Institute of Science Tokyo (Science Tokyo), Japan, have developed a method to make these models more interpretable. Their approach works by analyzing a trained AI model and extracting the key features it has learned about how crystal structure relates to optical spectra. Using these features, the researchers then grouped materials that share similar optical spectra and structural characteristics.<\/p>\n\n\n\n

The study was led by Akira Takahashi, now an Associate Professor, Professor Fumiyasu Oba (also a project leader at KISTEC, Japan), and Master\u2019s course student Arata Takamatsu (at the time of the research) of the Materials and Structures Laboratory, Science Tokyo, in collaboration with Professor Yu Kumagai of the Institute for Materials Research, Tohoku University, Japan.<\/p>\n\n\n\n

“Our proposed classification method allows for an understanding in detail of how AI prediction models make predictions, namely, extracting key factors for desired spectral shapes, and thereby providing useful physical and chemical insights for materials design,” says Takahashi.<\/p>\n\n\n\n

Material\u2019s properties often depend on some parameters and are described using spectral data\u2014for example, optical absorption spectra capturing how light interacts with the material across different wavelengths. Compared to properties represented by a single number, spectral data are far richer and more complex, making them challenging to interpret using conventional AI methods.<\/p>\n\n\n\n

The researchers used an atomistic line graph neural network (ALIGNN), an existing graph neural network architecture, trained to predict optical absorption spectra from atomic structure using data from 2,681 metal oxides, chalcogenides, and related compounds. From the trained model, they extracted features from its internal layers and applied hierarchical clustering, a method that groups items based on similarity. This allowed them to classify materials into distinct groups that shared both structural features, such as elemental composition, atomic coordination, bond lengths, and bond angles, and similar spectral shapes.<\/p>\n\n\n\n

Notably, the model learned these patterns from atomic structure alone, without being given oxidation states or electronic configurations as input, indicating that it had internally captured meaningful relationships between structure and properties.<\/p>\n\n\n\n

Optical properties play a key role in many applications. They affect a material\u2019s appearance, which is important for pigments and dyes, and govern how it interacts with light in devices such as solar cells and photodetectors. Understanding which elemental species and structural features shape these spectra is, therefore, key to establishing rational design guidelines for such materials.<\/p>\n\n\n\n

Furthermore, the approach is not limited to optical spectra: it can be extended to determine how a material\u2019s structure influences its behavior under different conditions, such as temperature or pressure, opening up new possibilities for designing materials with specific and useful properties. As demonstrated here for optical absorption, the approach can be applied to a range of spectral properties, enabling researchers to identify common factors shared by different materials and infer the origins of desired spectral characteristics.<\/p>\n\n\n\n

“It has been difficult to interpret what machine learning models have learned about spectral properties. In this work, we developed a general method to extract such insights, which we believe will prove broadly useful for materials research,” concludes Takahashi.<\/p>\n","protected":false},"excerpt":{"rendered":"

In recent years, artificial intelligence (AI) has emerged as a powerful tool to predict how materials will behave based on their atomic structure, helping researchers discover new materials faster and reduce reliance on trial-and-error methods. <\/p>\n","protected":false},"author":2,"featured_media":38361,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17,163],"tags":[],"class_list":["post-38359","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research","category-ai"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research.webp",768,576,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research-200x200.webp",200,200,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research-675x506.webp",675,506,true],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research.webp",750,563,false],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research.webp",750,563,false],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research.webp",768,576,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research.webp",768,576,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research.webp",768,576,false],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research-768x570.webp",768,570,true],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research-600x576.webp",600,576,true],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research-600x576.webp",600,576,true],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research-760x490.webp",760,490,true],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research-550x360.webp",550,360,true],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research-95x65.webp",95,65,true],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research-640x576.webp",640,576,true],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research-96x96.webp",96,96,true],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2026\/07\/ai-research-150x113.webp",150,113,true]},"author_info":{"info":["RevoScience"]},"category_info":"Research<\/a> AI<\/a>","tag_info":"AI","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/38359","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=38359"}],"version-history":[{"count":1,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/38359\/revisions"}],"predecessor-version":[{"id":38362,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/38359\/revisions\/38362"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/38361"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=38359"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=38359"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=38359"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}