| Dec 29, 2025 |
Researchers use AI to visualize reaction pathways of colloidal quantum dots as subway maps, clarifying synthesis and speeding sensor design.
(Nanowerk News) Professor Joongoo Kang’s team from the Department of Physics and Chemistry at DGIST (President Kunwoo Lee) and Professor Sohee Jeong’s team from the Department of Energy Science at Sungkyunkwan University developed a technology that visualizes the synthetic reaction pathways of semiconductor nanocrystals (colloidal quantum dots) using artificial intelligence (AI).
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This innovative achievement allows AI to analyze complex chemical reaction flows, which are difficult to understand through experiments alone, and to display them intuitively like a ‘subway map.’ It is expected to significantly accelerate development of next-generation display and sensor materials.
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The research has been published in Journal of the American Chemical Society (“Topological Machine Learning Unveils Hidden Reaction Pathways in Nanocrystal Synthesis”).
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| Conceptual comparison between conventional, researcher-dependent interpretation of optical spectra and an AI-based approach that learns directly from data. The lower pathway shows how training maps spectral measurements into a high-dimensional feature space, forms a uniform manifold representation, and applies dimensionality reduction to enable objective visualization of reaction or response pathways. (Image: DGIST) (click on image to enlarge)
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Semiconductor nanocrystals (colloidal quantum dots) are nanometer-sized semiconductor particles and next-generation nanomaterials whose absorption and emission color and intensity can be precisely controlled by size. They are a key material for high-color reproducibility displays, attracting attention from global companies like Samsung Display as innovative quantum dot luminescent materials. Their significance is also increasing in the field of infrared cameras and sensors.
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However, investigating and revealing the steps involved in the formation of each nanocrystal is very challenging. Previously, researchers had to estimate reaction pathways using a method similar to ‘inference’ based on limited experimental data, which limited their ability to interpret results accurately due to data insufficiency and complex reaction behavior.
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To address this issue, the research team combined ‘Transformer’-based AI, renowned as the latest natural language processing technology, with ‘topological data analysis.’ Using this approach, the AI automatically completes incomplete data to accurately reconstruct the entire reaction flow and identify structural connections between different data sets. Through this, the research team successfully visualized the complex reaction process as a single ‘map.’
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The team used this technology to synthesize InAs (indium arsenide) nanocrystals, a next-generation infrared semiconductor material, and confirmed that the growth pathway, previously thought to be single, actually branches into multiple pathways. They also discovered that materials added during synthesis act as ‘traffic lights’ and are crucial in determining the reaction flow.
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Professor Joongoo Kang said, “This study is a significant achievement demonstrating that AI can act as an ‘invisible navigation’ to uncover hidden pathways in chemical reactions that are difficult for humans to observe.” Professor Sohee Jeong expressed her expectations, “This technology will significantly enhance research efficiency in various new fields in material development.”
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