Paper
†: Co-first author, *: Corresponding author
Submitted
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Kim, D.†*, Miao, N.†, Jung, J.†, Jung, T. S., Zhou, Y., Lee, S., Schön, C.-F., Yu, Y., Kim, J. H.*, & Han, S.*
Glass-transition-driven electron delocalization underpins volatile switching in amorphous chalcogenides
2026
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Kim, J., Cho, H., Jeon, H., Jung, J.*, & Han, S.*
Reactive machine learning interatomic potentials for chemistry and materials science
Chemical Reviews (accepted)
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Kim, S., Jeon, G., Hwang, S., Lee, J., Jung, J., Han, S., & Kang, S.*
Are diffusion models ready for materials discovery in unexplored chemical space?
Patterns (accepted)
2025
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Kim, D.†, Kim, S.†, Jung, J.†, Kim, J., Choi, S., Schön, C.-F., Lee, C., Lim, H., Jeong, J., Yu, S., Jeong, Y., Lee, H., Kim, S., Nam, D., Eom, I., Jang, D., Kim, K. S., Im, S., Han, S.*, Kim, H.*, & Cho, M.-H.*
Nonmelting disordering facilitated by electron delocalization
ACS nano 19, 9317-9326 (2025)
2024
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Jung, J., An, H., Lee, J. & Han, S.*
Modified activation-relaxation technique (ARTn) method tuned for efficient identification of transition states in surface reactions
J. Chem. Theory Comput. 20, 8024 (2024)
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Lee, J.†, Ju, S.†, Hwang, S., You, J., Jung, J., Kang, Y. & Han, S.*
Disorder-dependent Li diffusion in Li6PS5Cl investigated by machine learning potential
ACS Appl. Mater. Interface 16, 46442 (2024)
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Kim, J.†, Jung, J.†, Kim, S. & Han, S.*
Predicting melting temperature of inorganic crystals via crystal graph neural network enhanced by transfer learning
Comput. Mater. Sci. 234, 112783 (2024)
2023
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Hong, C.†, Kim, J.†, Kim, J., Jung, J., Ju, S., Choi, J. M. & Han, S.*
Applications and training sets of machine learning potentials
Sci. Technol. Adv. Mater.: Methods 3, 2269948 (2023)
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Jung, J.†, Ju, S.†, Kim, P.-H., Hong, D., Jeong, W., Lee, J., Han, S.* & Kang, S.*
Electrochemical degradation of Pt3Co nanoparticles investigated by off-lattice kinetic Monte Carlo simulations with machine-learned potentials
ACS Catal. 13, 16078–16087 (2023)
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Hwang, S., Jung, J., Hong, C., Jeong, W., Kang, S.* & Han, S.*
Stability and equilibrium structures of unknown ternary metal oxides explored by machine-learned potentials
J. Am. Chem. Soc. 145, 19378–19386 (2023) [Supplementary cover]
2022
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Kim, S., An, H., Oh, S., Jung, J., Kim, B., Nam, S. K. & Han, S.*
Atomistic kinetic Monte Carlo simulation on atomic layer deposition of TiN thin film
Comput. Mater. Sci. 231, 111620 (2022)
2021
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Yoo, D.†, Jung, J.†, Jeong, W. & Han, S.*
Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials
npj Comput. Mater. 7, 131 (2021)
2020
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Hong, C.†, Choi, J. M.†, Jeong, W.†, Kang, S.†, Ju, S., Lee, K., Jung, J., Youn, Y. & Han, S.*
Training machine-learning potentials for crystal structure prediction using disordered structures
Phys. Rev. B 102, 224104 (2020)
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Youn, Y., Lee, M., Hong, C., Kim, D., Kim, S., Jung, J., Kim, Y. & Han, S.*
AMP2: A fully automated program for ab initio calculations of crystalline materials
Comput. Phys. Commun. 256, 107450 (2020)
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Jeong, W., Yoo, D., Lee, K., Jung, J. & Han, S.*
Efficient atomic-resolution uncertainty estimation for neural network potentials using a replica ensemble
J. Phys. Chem. Lett. 11, 6090–6096 (2020) [Supplementary cover]