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Exploring materials data through collaboration: 2024 KRICT ChemDX Hackathon

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J Mater Inf 2025;5:[Accepted].
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Abstract

Data-driven research is in the spotlight across many science and engineering fields, in-cluding materials science, with the expectation that effective utilization of data, assisted by modern artificial intelligence techniques, can lead to breakthroughs in addressing key scientific questions. KRICT Chemical Data Explorer platform (ChemDX), our web-based and integrated platform including various data explorer and artificial intelligence modules, aims to enhance accessibility of chemical data for digital materials discovery. In this arti-cle, we highlight the results of the 2024 KRICT ChemDX Hackathon, an event to support data-driven research in chemistry and materials science. Hackathon participants explored ChemDX platform and developed projects ranging from machine learning models and data visualization tools to user interface improvements. These projects demonstrated the versa-tility and potential of data-driven research with the aid of ChemDX platform, in bridging data-driven experimental and computational research. The feedback and outcomes from this hackathon demonstrate the impressive potential of interdisciplinary data-driven research, guide further improvements to the platform, and enhance its usability and outreach.

Keywords

Machine learning, database, hackathon, materials discovery

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Yoo SH, Low AKY, Recatala-Gomez J, Sahu H, Kim C, Joung JF, Chun H, Christofidou KA, Berry J, Minotakis M, Kang K, Kim K, Shin G, Jang H, Lee S, Park M, Kim BH, Shin K, Shin J, Soon A, Schrier J, Jang W. Exploring materials data through collaboration: 2024 KRICT ChemDX Hackathon. J Mater Inf 2025;5:[Accept]. http://dx.doi.org/10.20517/jmi.2025.65

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© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Journal of Materials Informatics
ISSN 2770-372X (Online)
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