K
Kwang S. Kim
Researcher at Ulsan National Institute of Science and Technology
Publications - 671
Citations - 71259
Kwang S. Kim is an academic researcher from Ulsan National Institute of Science and Technology. The author has contributed to research in topics: Graphene & Ab initio. The author has an hindex of 97, co-authored 642 publications receiving 62053 citations. Previous affiliations of Kwang S. Kim include Asia Pacific Center for Theoretical Physics & IBM.
Papers
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Aqua dissociation nature of cesium hydroxide.
TL;DR: Hydrated structures, stabilities, thermodynamic quantities, dissociation energies, infrared spectra, and electronic properties of CsOH(H(2)O)(n=0-4) are reported.
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Description of ground and excited electronic states by ensemble density functional method with extended active space.
TL;DR: The new method, the state-interaction state-averaged REKS(4,4), is capable of describing several excited states of a molecule involving double bond cleavage, polyradical character, or multiple chromophoric units and can describe a wide range of multireference phenomena.
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An ultra-sensitive, flexible and transparent gas detection film based on well-ordered flat polypyrrole on single-layered graphene
Taeseung Yoon,Jaemoon Jun,Dong Yeon Kim,Saeed Pourasad,Tae Joo Shin,Seong Uk Yu,Wonjoo Na,Jyongsik Jang,Kwang S. Kim +8 more
TL;DR: In this paper, a flexible and transparent sub-ppb gas detection film fabricated by in situ electrochemical oxidative polymerization on single-layer graphene (SLG) is presented.
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Quencher-free molecular beacon: Enhancement of the signal-to-background ratio with graphene oxide.
TL;DR: This QF-MB/GO system provided a higher S/B ratio relative to that of the same system in the absence of GO, while retaining a high selectivity for fully matched over single-base-mismatched targets.
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Sparse Gaussian process potentials: Application to lithium diffusivity in superionic conducting solid electrolytes
TL;DR: In this paper, a scalable sparse Gaussian process regression formalism is introduced with a data-efficient on-the-fly adaptive sampling algorithm for machine learning of interatomic potentials.