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Seo, Dong-Hwa
Computational Energy Materials Science Lab
Research Interests
  • 본 연구실에서는 제일원리 (first-principles) 전산모사 기법을 통해 이차전지용 전극 소재와 고체 전해질 소재에 대해 원자 단위에서 깊이 있게 이해하고 이를 바탕으로 신규 소재를 개발하고 기존 소재의 성능 향상시키는 연구를 진행하고 있습니다. 또한 인공지능 (artificial intelligence)과 기계학습 (Machine learning), 로봇공학 (robotics)을 조합하여 자동 합성/분석을 통한 재료 개발에 대한 연구를 진행하고 있습니다.

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Computational understanding of Li-ion batteries

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Title
Computational understanding of Li-ion batteries
Author
Urban, AlexanderSeo, Dong-HwaCeder, Gerbrand
Issue Date
2016-20
Publisher
SPRINGERNATURE
Citation
NPJ COMPUTATIONAL MATERIALS, v.2, pp.16002
Abstract
Over the last two decades, computational methods have made tremendous advances, and today many key properties of lithium-ion batteries can be accurately predicted by first principles calculations. For this reason, computations have become a cornerstone of battery-related research by providing insight into fundamental processes that are not otherwise accessible, such as ionic diffusion mechanisms and electronic structure effects, as well as a quantitative comparison with experimental results. The aim of this review is to provide an overview of state-of-the-art ab initio approaches for the modelling of battery materials. We consider techniques for the computation of equilibrium cell voltages, 0-Kelvin and finite-temperature voltage profiles, ionic mobility and thermal and electrolyte stability. The strengths and weaknesses of different electronic structure methods, such as DFT+U and hybrid functionals, are discussed in the context of voltage and phase diagram predictions, and we review the merits of lattice models for the evaluation of finite-temperature thermodynamics and kinetics. With such a complete set of methods at hand, first principles calculations of ordered, crystalline solids, i.e., of most electrode materials and solid electrolytes, have become reliable and quantitative. However, the description of molecular materials and disordered or amorphous phases remains an important challenge. We highlight recent exciting progress in this area, especially regarding the modelling of organic electrolytes and solid-electrolyte interfaces.
URI
https://scholarworks.unist.ac.kr/handle/201301/30523
URL
https://www.nature.com/articles/npjcompumats20162
DOI
10.1038/npjcompumats.2016.2
ISSN
2057-3960
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ECHE_Journal Papers
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