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
Multifarious computational design of complex materials from first-principles
Modern machine learning methods and models for digital materials science
저희 연구실은 최신의 전산재료과학 및 재료이론 연구 방법론들을 고성능 슈퍼컴퓨터의 사용과 접목하여 차세대 기능성 신소재 설계 및 탐구를 진행하고 있습니다. 특히 중점적으로 연구하고 있는 연구 주제들은 다음과 같습니다.
다양한 복잡(complex) 재료의 표면 및 계면의 제일원리 기반 설계 및 분석
제일원리 전자구조이론 기반 다기능성 소재 설계
인공지능과 기계학습 기법 및 모델링을 활용한 디지털 재료과학
Ab initio characterization of surfaces and interfaces in complex materials
Representative publications
T. Lee and A. Soon, The rise of ab initio surface thermodynamics, Nat. Catal. (News & Views) 7, 4 (2024) [pdf_AM] View-only version (via SharedIt link)
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1038/s41929-023-01088-y
S. Lee, Y.-J. Lee, G. Lee, and A. Soon, Activated chemical bonds in nanoporous and amorphous iridium oxides favor low overpotential for oxygen evolution reaction, Nat. Commun. 13, 3171 (2022) [pdf] Open Access
News flash! Structure-property relationships in nanoporous and amorphous iridium oxides
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] View-only version (via SharedIt link)
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1038/s41563-020-00863-7
News flash! Liquid metal ink liberates form
Multifarious computational design of complex materials from first-principles
Representative publications
G. Heo, A. Soon, and T. Lee, Data-mining fluoride-based solid-state electrolytes for monovalent metal batteries, J. Mater. Chem. A 12, 27409 (2024) [pdf]
S. Acharya, J. Hwang, K. Kim, J. Kim, W. Hwang, A. Soon, and W. Kim, Quasi-random distribution of distorted nanostructures enhances thermoelectric performance of high-entropy chalcopyrite, Nano Energy 112, 108493 (2023) [pdf]
News flash! 소재 구성의 준랜덤(quasi-random) 분포를 통해 열전 성능 지수 132% 증진 | 소재 분야 권위 있는 국제 학술지 ‘Nano Energy’ 게재
S. Cha, G. Lee, S. Lee, S. H. Ryu, G. An, C. Kang, M. Kim, Y. Sohn, K. Kim, A. Soon, and K. S. Kim, Order-disorder phase transition driven by interlayer sliding in lead iodides, Nat. Commun. 14, 1981 (2023) [pdf] Open Access
K. Kim, W. Hwang, J.-H. Lee, and A. Soon, Explicating the irreversible electric-field-assisted ferroelectric phase transition in the otherwise antiferroelectric sodium niobate for energy storage systems, J. Mater. Chem. C 10, 10500 (2022) [pdf]
This article is part of the themed collection: Journal of Materials Chemistry C HOT Papers
Modern machine learning methods and models for digital materials science
Representative publications
W. Hwang, S.-H. V. Oh, J. Shin, A. Soon, S.-H. Yoo, and W. Jang, Data-driven materials informatics for novel piezoelectric Janus-type nanomaterials discovery, J. Phys. Chem. Lett. 15, 6451 (2024) [pdf]
H. Kim, G. Lee, S.-H. V. Oh, C. Stampfl, and A. Soon, Recalibrating the experimentally derived structure of the metastable surface oxide on copper via machine learning-accelerated in silico global optimization, ACS Nano 18, 4559 (2024) [pdf]
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]