{"id":14533,"date":"2026-11-08T05:12:13","date_gmt":"2026-11-08T05:12:13","guid":{"rendered":"https:\/\/sotnews.agency\/?p=14533"},"modified":"2026-11-08T05:12:13","modified_gmt":"2026-11-08T05:12:13","slug":"kookmin-university-research-team-develops-ai-model-to-predict-properties-of-mixtures","status":"publish","type":"post","link":"https:\/\/sotnews.agency\/?p=14533","title":{"rendered":"Kookmin University research team develops AI model to predict properties of mixtures"},"content":{"rendered":"
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From left are Joung Joon-young, a chemistry professor at Kookmin University\u2019s Graduate School; Kim Ji-hwan, a student in the university\u2019s integrated master\u2019s and doctoral program; and Kim Chan-joong, an undergraduate student. Courtesy of Kookmin University<\/p>\n<\/figcaption><\/figure>\n

A Kookmin University research team has developed an artificial intelligence (AI) model that predicts the properties of mixtures based on the proportions of their chemical components.<\/p>\n

The university said Thursday that the team was led by Joung Joon-young, a professor in the Department of Chemistry at the university\u2019s Graduate School.<\/p>\n

The team included Kim Ji-hwan, a student in the university\u2019s integrated master\u2019s and doctoral program, and Kim Chan-joong, an undergraduate student.<\/p>\n

The findings were published in the Chemical Engineering Journal under the title \u201cEncoding composition-dependent intermolecular interactions into learned molecular representations.\u201d<\/p>\n

Most chemical products people encounter in everyday life are mixtures of multiple substances rather than single compounds. Common examples include industrial solvents, electrolytes in batteries, automotive fuels, and cosmetic and pharmaceutical formulations.<\/p>\n

The properties of a mixture depend not only on which substances it contains but also on the proportions in which they are combined. These changes, however, do not necessarily occur in direct proportion to the mixing ratios.<\/p>\n

Conventional AI models have typically analyzed mixtures by first learning the properties of individual molecules and then calculating a weighted average based on their respective mixing ratios.<\/p>\n

Such an approach has limitations in capturing how molecules interact with one another and how these interactions change depending on the composition of the mixture.<\/p>\n

To address these limitations, Joung\u2019s team developed a new AI model called Mixture Embedding via Mole Fraction (MEMo).<\/p>\n

MEMo is designed to account for both the identity of each molecule and its mixing proportions from the earliest stages of molecular analysis.<\/p>\n

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An overview of Mixture Embedding via Mole Fraction, an artificial intelligence model that predicts the properties of mixtures containing multiple chemical substances \/ Courtesy of Kookmin University <\/p>\n<\/figcaption><\/figure>\n

In the initial stages, the model identifies the characteristics of individual molecules. In subsequent stages, the model allows different molecules to exchange information, enabling it to learn how they interact.<\/p>\n

This enables the model to recognize that a molecule\u2019s behavior can vary depending on which other molecules it interacts with and in what proportions.<\/p>\n

The team used more than 860,000 experimental measurements from the U.S. National Institute of Standards and Technology to evaluate the model\u2019s predictive performance across five properties: density, speed of sound, vapor pressure, refractive index and surface tension.<\/p>\n

MEMo outperformed conventional methods in predicting four of the five properties.<\/p>\n

The improvement was particularly pronounced for mixtures with strong intermolecular interactions, such as ionic liquids and aqueous salt solutions, as well as those containing molecules the model had not encountered during training.<\/p>\n

\u201cThe properties of a mixture depend on which molecules interact with one another and in what proportions,\u201d Joung said. \u201cThe key to this research was developing an AI model that understands molecular behavior based on the same principle.\u201d<\/p>\n

He expects the model to reduce the time and cost of materials development by rapidly predicting the properties of mixtures with different compositions without requiring physical experiments.<\/p>\n

Joung said the model could find applications in various fields involving mixtures, including battery electrolyte design, the development of eco-friendly solvents and the discovery of materials for carbon dioxide capture.<\/p>\n

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From left are Joung Joon-young, a chemistry professor at Kookmin University\u2019s Graduate School; Kim Ji-hwan, a student in the university\u2019s integrated master\u2019s and doctoral program; and Kim Chan-joong, an undergraduate student. Courtesy of Kookmin University A Kookmin University research team has developed an artificial intelligence (AI) model that predicts the properties of mixtures based on…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rop_custom_images_group":[],"rop_custom_messages_group":[],"rop_publish_now":"initial","rop_publish_now_accounts":{"facebook_2277560469115098_106292521332774":"","twitter_aToxNzczMzI3Njk4OTg4ODUxMjAxOw==_1773327698988851200":""},"rop_publish_now_history":[],"rop_publish_now_status":"pending","_monsterinsights_skip_tracking":false,"_templately_pack_id":"","_templately_imported_at":"","_templately_source":"","_templately_import_session_id":"","footnotes":""},"categories":[16],"tags":[],"class_list":["post-14533","post","type-post","status-publish","format-standard","category-korea"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t