Jisu Jung

About Me

Hello! I am Jisu Jung, a Ph. D. candidate in the Materials Data & Informatics Laboratory at Seoul National University. I am advised by Prof. Seungwu Han.

I have a great interest in machine learning techniques on atomistic modeling. In my research, I have developed and applied machine learning intereatomic potentials.

Research

  • Density functional theory calculation
  • Machine-learning interatomic potential
  • Kinetic Monte Carlo simulation
  • Artificial intelligence for material science

Education

Ph. D. Material science and Engineering Mar 2019 — Present

Seoul National University

Advisor: Seungwu Han

B. S. Material science and Engineering Mar 2013 — Feb 2019

Seoul National University

Research experience

Visiting student Jul 2024 — Aug 2024

Imperial College London

Advisor: Aron Walsh

Publication

  1. 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 Comp. (accepted)
  2. 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) [paper]
  3. 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) [paper]
  4. 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) [paper]
  5. 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) [paper]
  6. 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) [paper]
  7. 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) [paper]
  8. 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) [paper]
  9. 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) [paper]
  10. 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) [paper]
  11. 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) [paper]

Experience

Teaching Assistant Spring 2019

Seoul National University

Thermodynamics of Materials

Teaching Assistant Spring 2020

Seoul National University

Engineering Mathematics

Teaching Assistant Spring 2020, Fall 2021

Seoul National University

Self-design Experiments in Materials

Teaching Assistant Spring 2021, Fall 2022

Seoul National University

Modeling and Simulation of Materials

Teaching Assistant Jan 2022

Korea Institute for Advanced Study

Electronic Structure Calculations Winter School

(Neural network potential: Practical theory and its applications)

Contact

For inquiries or collaborations, please feel free to contact me via e-mail.