Hasil Pencarian - Kyoungmin Min

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  1. 1
    A Comparative Study of the Performance for Predicting Biodegradability Classification: The Quantitative Structure–Activity Relationship Model vs the Graph Convolutional Network

    A Comparative Study of the Performance for Predicting Biodegradability Classification: The Quantitative Structure–Activity Relationship Model vs the Graph Convolutional Network oleh Myeonghun Lee, Kyoungmin Min

    Diterbitkan 2022-01-01
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  2. 2
    Predicting mechanical properties of newly generated two-dimensional materials with minimum uncertainty

    Predicting mechanical properties of newly generated two-dimensional materials with minimum uncertainty oleh Inhyo Lee, Joonchul Kim, Taehyun Park, Kyoungmin Min

    Diterbitkan 2023-06-01
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  3. 3
    Correlating atomistic characteristics of zeolites to their 3D-Printed Macro structural properties for prediction of mechanical response

    Correlating atomistic characteristics of zeolites to their 3D-Printed Macro structural properties for prediction of mechanical response oleh Joonchul Kim, Minseong Hong, JunHo Song, Namjung Kim, Kyoungmin Min

    Diterbitkan 2023-09-01
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  4. 4
    Improved electrochemical properties of LiNi0.91Co0.06Mn0.03O2 cathode material via Li-reactive coating with metal phosphates

    Improved electrochemical properties of LiNi0.91Co0.06Mn0.03O2 cathode material via Li-reactive coating with metal phosphates oleh Kyoungmin Min, Kwangjin Park, Seong Yong Park, Seung-Woo Seo, Byungjin Choi, Eunseog Cho

    Diterbitkan 2017-08-01
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  5. 5
    Toward fast and accurate machine learning interatomic potentials for atomic layer deposition precursors

    Toward fast and accurate machine learning interatomic potentials for atomic layer deposition precursors oleh Seungpyo Kang, Joonchul Kim, Taehyun Park, Joonghee Won, Chul Baik, Jungim Han, Kyoungmin Min

    Diterbitkan 2024-03-01
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  6. 6
    Atomistic insights into adhesion characteristics of tungsten on titanium nitride using steered molecular dynamics with machine learning interatomic potential

    Atomistic insights into adhesion characteristics of tungsten on titanium nitride using steered molecular dynamics with machine learning interatomic potential oleh Eunseog Cho, Won-Joon Son, Eunae Cho, Inkook Jang, Dae Sin Kim, Kyoungmin Min

    Diterbitkan 2023-10-01
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  7. 7
    Interpretable machine learning boosting the discovery of targeted organometallic compounds with optimal bandgap

    Interpretable machine learning boosting the discovery of targeted organometallic compounds with optimal bandgap oleh Taehyun Park, JunHo Song, Jinyoung Jeong, Seungpyo Kang, Joonchul Kim, Joonghee Won, Jungim Han, Kyoungmin Min

    Diterbitkan 2024-08-01
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