Our research focuses on the design and discovery of novel advanced materials using state-of-the-art computational and theoretical methods, broadly classified into these research themes:
Ab initio characterization of surfaces and interfaces in complex materials
Representative publications:
- Y.-J. Lee, T. T. Ly, T. Lee, K. Palotás, S. Y. Jeong, J. Kim, and A. Soon, Completing the picture of initial oxidation on copper, Appl. Surf. Sci. 562, 150148 (2021) [pdf] Open Access
- S. Veerapandian, W. Jang, J. B. Seol, H. Wang, M. Kong, K. Thiyagarajan, J. Kwak, G. Park, G. Lee, W. Suh, I. You, M. E. Kılıç, A. Giri, L. Beccai, A. Soon, and U. Jeong, Hydrogen-doped viscoplastic liquid metal microparticles for stretchable printed metal lines, Nat. Mater. 20, 533 (2021) [pdf]
DOI | View-only version (via SharedIt link) - T. Lee, Y.-J. Lee, K. Palotás, G. Lee, C. Stampfl, and A. Soon, Polymorphic expressions of ultrathin oxidic layers of Mo on Au(111), Nanoscale 11, 6023 (2019) [pdf]
- T. Lee, Y. Lee, S. Piccinin, and A. Soon, Ab initio thermodynamics of surface oxide structures under controlled growth conditions, J. Phys. Chem. C 121, 2228 (2017) [pdf]
Multifarious computational design of complex materials from first-principles
Representative publications:
- Y.-J. Lee, M. Han, S.-H. Yoo, and A. Soon, Tunable threshold voltage of ZnTe-based ovonic switching devices via isovalent cation-exchange, ACS Appl. Electron. Mater. 3, 1107 (2021) [pdf]
- W. Hwang, J.-H. Lee, and A. Soon, Enhanced polarization in epitaxially strained monoclinic potassium niobate for lead-free electromechanical applications, J. Mater. Chem. C 9, 13420 (2021) [pdf]
- J. Lee, H. Kim, T. Lee, W. Jang, K. H. Lee, and A. Soon, Revisiting polytypism in hexagonal ternary sulfide ZnIn2S4 for photocatalytic hydrogen production within the Z-scheme, Chem. Mater. 31, 9148 (2019) [pdf]
- Y.-J. Lee, T. Lee, and A. Soon, Phase stability diagrams of Group 6 Magnéli oxides and their implications for photon-assisted applications, Chem. Mater. 31, 4282 (2019) [pdf]
Modern machine learning methods and models in digital materials science
Representative publications:
- S.-H. V. Oh, W. Hwang, K. Kim, J.-H. Lee, and A. Soon, Using feature-assisted machine learning algorithms to boost polarity in lead-free multicomponent niobate alloys for high-performance ferroelectrics, Adv. Sci. 9, 2104569 (2022) [pdf] Open Access
- S.-H. Yoo, J.-H. Lee, Y.-K. Jung, and A. Soon, Exploring stereographic surface energy maps of cubic metals via an effective pair-potential approach, Phys. Rev. B 93, 035434 (2016) [pdf]