☰ Chemistry Tree

Yousung Jung, Ph.D.

Affiliations:

Korea Advanced Institute of Science and Technology, Daejeon, South Korea

Area:

Theoretical Chemistry

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Mean distance: 8.7

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Parents

Doo Soo Chung	research assistant	1999	Seoul National University
Mark S. Gordon	research assistant	2001	Iowa State
Martin Head-Gordon	grad student	2005	UC Berkeley
(Correlated ab initio methods for the study of large molecules: Theory, algorithms, and applications.)
Rudolph A. Marcus	post-doc	2005-2009	Caltech

Children

Seoin Back	grad student	2017	KAIST (E-Tree)
Manjaly J. Ajitha	post-doc	2013-2014	KAIST
Geun Ho Gu	post-doc	2018-2022	KAIST

Collaborators

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Publications

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Kim K, Lim S, Shin K, et al. (2024) Variational quantum eigensolver for closed-shell molecules with non-bosonic corrections. Physical Chemistry Chemical Physics : Pccp
Back S, Aspuru-Guzik A, Ceriotti M, et al. (2023) Accelerated chemical science with AI. Digital Discovery. 3: 23-33
Yun Y, Khaliullin RZ, Jung Y. (2022) Correlated Local Fluctuations in the Hydrogen Bond Network of Liquid Water. Journal of the American Chemical Society
Gu GH, Lee M, Jung Y, et al. (2022) Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility. Nature Communications. 13: 2087
Kim S, Noh J, Gu GH, et al. (2022) Correction to "Generative Adversarial Networks for Crystal Structure Prediction". Acs Central Science. 8: 402
Epifanovsky E, Gilbert ATB, Feng X, et al. (2021) Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package. The Journal of Chemical Physics. 155: 084801
Park J, Cho M, Rhee YM, et al. (2021) Theoretical Study on the Degree of CO Activation in CO-Coordinated Ni(0) Complexes. Acs Omega. 6: 7646-7654
Kim S, Noh J, Gu GH, et al. (2020) Generative Adversarial Networks for Crystal Structure Prediction. Acs Central Science. 6: 1412-1420
Gu GH, Choi C, Lee Y, et al. (2020) Progress in Computational and Machine-Learning Methods for Heterogeneous Small-Molecule Activation. Advanced Materials (Deerfield Beach, Fla.). e1907865
Gu GH, Noh J, Kim S, et al. (2020) Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening. The Journal of Physical Chemistry Letters

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