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Kyoungmin Min

Showing results (11-20 of 45) with videos related to

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Physical Chemistry Chemical Physics : PCCP|March 8, 2022
Prediction of dielectric constants of ABO<sub>3</sub>-type perovskites using machine learning and first-principles calculationsEunsong Kim, Joonchul Kim, Kyoungmin Min
The Journal of Physical Chemistry. A|November 12, 2021
Adaptive Learning Framework in Prediction and Validation of Gibbs Free Energy for Inorganic Crystalline SolidsJonghoon Yoon, Eunseong Choi, Kyoungmin Min
Journal of Chemical Information and Modeling|November 21, 2024
Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network DesignMyeonghun Lee, Taehyun Park, Kyoungmin Min
ACS Applied Materials & Interfaces|July 27, 2023
Screening Platform for Promising Na Superionic Conductors for Na-Ion Solid-State ElectrolytesJuo Kim, Seungpyo Kang, Kyoungmin Min
ACS Applied Materials & Interfaces|January 18, 2022
A Synergistic Effect of Na<sup>+</sup> and Al<sup>3+</sup> Dual Doping on Electrochemical Performance and Structural Stability of LiNi<sub>0.88</sub>Co<sub>0.08</sub>Mn<sub>0.04</sub>O<sub>2</sub> Cathodes for Li-Ion BatteriesHyun Gyu Park, Kyoungmin Min, Kwangjin Park
ACS Applied Materials & Interfaces|September 13, 2017
Theoretical Prediction of Surface Stability and Morphology of LiNiO<sub>2</sub> Cathode for Li Ion BatteriesEunseog Cho, Seung-Woo Seo, Kyoungmin Min
ACS Applied Materials & Interfaces|October 10, 2018
Characterization of Mechanical Degradation in Perfluoropolyether Film for Its Application to Antifingerprint CoatingsKyoungmin Min, Jungim Han, Byungha Park, et al.
ACS Applied Materials & Interfaces|January 19, 2023
Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine LearningJiwon Sun, Seungpyo Kang, Joonchul Kim, et al.
ACS Applied Materials & Interfaces|May 2, 2024
Machine-Learning-Driven High-Throughput Screening for High-Energy Density and Stable NASICON CathodesJinyoung Jeong, Juo Kim, Jiwon Sun, et al.
Scientific Reports|October 27, 2018
Machine learning assisted optimization of electrochemical properties for Ni-rich cathode materialsKyoungmin Min, Byungjin Choi, Kwangjin Park, et al.
Pageof 5

Showing results (11-20 of 45) with videos related to

Sort By:
Pageof 5
Physical Chemistry Chemical Physics : PCCP|March 8, 2022
Prediction of dielectric constants of ABO<sub>3</sub>-type perovskites using machine learning and first-principles calculationsEunsong Kim, Joonchul Kim, Kyoungmin Min
The Journal of Physical Chemistry. A|November 12, 2021
Adaptive Learning Framework in Prediction and Validation of Gibbs Free Energy for Inorganic Crystalline SolidsJonghoon Yoon, Eunseong Choi, Kyoungmin Min
Journal of Chemical Information and Modeling|November 21, 2024
Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network DesignMyeonghun Lee, Taehyun Park, Kyoungmin Min
ACS Applied Materials & Interfaces|July 27, 2023
Screening Platform for Promising Na Superionic Conductors for Na-Ion Solid-State ElectrolytesJuo Kim, Seungpyo Kang, Kyoungmin Min
ACS Applied Materials & Interfaces|January 18, 2022
A Synergistic Effect of Na<sup>+</sup> and Al<sup>3+</sup> Dual Doping on Electrochemical Performance and Structural Stability of LiNi<sub>0.88</sub>Co<sub>0.08</sub>Mn<sub>0.04</sub>O<sub>2</sub> Cathodes for Li-Ion BatteriesHyun Gyu Park, Kyoungmin Min, Kwangjin Park
ACS Applied Materials & Interfaces|September 13, 2017
Theoretical Prediction of Surface Stability and Morphology of LiNiO<sub>2</sub> Cathode for Li Ion BatteriesEunseog Cho, Seung-Woo Seo, Kyoungmin Min
ACS Applied Materials & Interfaces|October 10, 2018
Characterization of Mechanical Degradation in Perfluoropolyether Film for Its Application to Antifingerprint CoatingsKyoungmin Min, Jungim Han, Byungha Park, et al.
ACS Applied Materials & Interfaces|January 19, 2023
Accelerated Discovery of Novel Garnet-Type Solid-State Electrolyte Candidates via Machine LearningJiwon Sun, Seungpyo Kang, Joonchul Kim, et al.
ACS Applied Materials & Interfaces|May 2, 2024
Machine-Learning-Driven High-Throughput Screening for High-Energy Density and Stable NASICON CathodesJinyoung Jeong, Juo Kim, Jiwon Sun, et al.
Scientific Reports|October 27, 2018
Machine learning assisted optimization of electrochemical properties for Ni-rich cathode materialsKyoungmin Min, Byungjin Choi, Kwangjin Park, et al.
Pageof 5