Changwoo Myung
Sungkyunkwan University · 材料科学
研究室紹介
Professor Changwoo Myung's research lab specializes in computational materials science and catalysis, focusing on the design and optimization of advanced materials for sustainable energy applications. The lab employs high-throughput first-principles calculations and machine learning to explore electrocatalysts for water splitting, ion diffusion and redox mechanisms in high-energy battery materials, and interfacial phenomena in perovskite solar cells. Key research directions include the development of non-precious metal electrocatalysts, understanding anion redox behavior in layered oxide cathodes, and engineering electron transport layers for stable and efficient optoelectronic devices. The integration of density functional theory with advanced machine learning techniques enables predictive insights into complex materials behavior at the atomic scale.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15High-performance 3d–5d transition metal single atom electrocatalysts ligated by various –N<sub>x</sub>C<sub>y</sub> moieties of N-doped graphene are investigated for hydrogen evolution and oxygen evolution/reduction reactions using high-throughput computational screening and machine learning.
Abstract For efficient water splitting, it is essential to develop inexpensive and super-efficient electrocatalysts for the oxygen evolution reaction (OER). Herein, we report a phosphate-based electrocatalyst [Fe 3 Co(PO 4 ) 4 @reduced-graphene-oxide(rGO)] showing outstanding OER performance (much higher than state-of-the-art Ir/C catalysts), the design of which was aided by first-principles calculations. This electrocatalyst displays low overpotential (237 mV at high current density 100 mA cm −
Abstract The anion redox reaction in high‐energy‐density cathode materials such as Li‐excess layered oxides suffers from voltage/capacity fadings due to irreversible structural instability. Here, exploiting density functional theory (DFT) as well as fast simulations using the universal potential/forces generated from the newly developed sparse Gaussian process regression (SGPR) machine learning (ML) method, the very complicated/complex structures, X‐ray absorption near‐edge‐structure (XANES) spe
Abstract SnO 2 electron transport layer (ETL) has been spotlighted with its excellent electron extraction and stability over TiO 2 ETL for perovskite solar cells (PSCs), rapidly approaching the highest power conversion efficiency. Thus, how to boost the performance of ETL is of utmost importance and of urgent need in developing more efficient PSCs. Here we elucidate the atomistic origin of efficient electron extraction and long stability of SnO 2 -based PSCs through the analysis of band alignmen
Recently, La-doped BaSnO<sub>3</sub> (LBSO) electron transport layer for perovskite solar cells has been introduced to replace TiO<sub>2</sub> which is susceptible to UV light. This paper unveils the key mechanism for the ideal band alignment between LBSO and MAPbI<sub>3</sub>. The amount of La dopant in LBSO is crucial for the fine tuning of the conduction band level of LBSO.
Abstract Metal halide perovskite (MHP) is a promising next generation energy material for various applications, such as solar cells, light emitting diodes, lasers, sensors, and transistors. MHPs show excellent mechanical, dielectric, photovoltaic, photoluminescence, and electronic properties, and such intriguing physical and chemical properties have drawn attention recently. However, there exists a chasm between the successful applications of MHPs and theoretical understandings. The difficulty a
Supersolid is a mysterious and puzzling state of matter whose possible existence has stirred a vigorous debate among physicists for over 60 years. Its elusive nature stems from the coexistence of two seemingly contradicting properties, long-range order and superfluidity. We report computational evidence of a supersolid phase of deuterium under high pressure (p>800 GPa) and low temperature (T<1.0 K). In our simulations, that are based on bosonic path integral molecular dynamics, we observe a high
Introducing active sparse Bayesian committee machine potentials with virial kernels for enhanced pressure accuracy. This enables efficient on-the-fly training for accurate isobaric machine learning molecular dynamics simulations with reduced costs.
The structure of oxide-supported metal nanoclusters plays an essential role in their sharply enhanced catalytic activity over that of bulk metals. Simulations provide the atomic-scale resolution needed to understand these systems. However, the sensitive mix of metal–metal and metal–support interactions, which govern their structure, puts stringent requirements on the method used, requiring calculations beyond standard density functional theory (DFT). The method of choice is coupled cluster theor
Abstract Traditionally, Fischer–Tropsch (FT) synthesis is performed using thermal catalysts and syngas (CO and H 2 ) under high‐pressure and high‐temperature conditions. However, this study introduces an approach that relies on FT chemistry assisted by electrochemistry, referred to here as direct electrochemical (EC) FT chemistry, under ambient conditions. A series of CH 4 , C n H 2n , and C n H 2n+2 hydrocarbons (n = 1–7) is successfully produced over gold (Au) nanoparticle‐loaded perovskite st
Excited-state molecular dynamics (ESMD) simulations near conical intersections (CIs) pose significant challenges when using machine learning potentials (MLPs). Although MLPs have gained recognition for their integration into mixed quantum-classical (MQC) methods, such as trajectory surface hopping (TSH), and their capacity to model correlated electron–nuclear dynamics efficiently, difficulties persist in managing nonadiabatic dynamics. Specifically, singularities at CIs and double-valued couplin
Abstract Closed‐shell light‐emitting diodes (LEDs) suffer from the internal quantum efficiency (IQE) limitation imposed by optically inactive triplet excitons. Here, an unrevealed emission mechanism of lead halide perovskites (LHPs) APbX 3 (A = Cs/CN 2 H 5 ; X = Cl/Br/I) that circumvents the efficiency limit of closed‐shell LEDs is explored. Though efficient emission is prohibited by optically inactive J = 0 in inversion symmetric LHPs, the anharmonicity arising from stereochemistry of Pb and re
ABSTRACT Anion exchange membrane fuel cells (AEMFCs) offer a sustainable energy solution with non‐precious metal catalysts, reduced degradation, and fuel flexibility. However, the sluggish oxygen reduction reaction (ORR) at the cathode and durability concerns impede commercialization. To address these challenges, this study presents a dual‐atomic SiFe–N–C catalyst derived from pinecones, a naturally abundant biomass resource. The catalyst features a nitrogen‐rich porous carbon matrix that stabil