Byung Chul Yeo

Pukyong National University — South Korea

Materials Science · Physical Sciences

21h-index1.2kcitations42works0.42yr avg
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Research Topics

Machine Learning in Materials Science(12), Advancements in Battery Materials(7), Electrocatalysts for Energy Conversion(7), Catalytic Processes in Materials Science(7), Ammonia Synthesis and Nitrogen Reduction(6)

Publications42 total

Quantitative analysis of operating conditions and performance trends in CO2 capture from large-scale literature
SSRN Electronic Journal·2026Open Access
Diffusion–Model–Driven Discovery of Ferroelectrics for Photocurrent Applications
2025
A Study on the Optimization of Fine-tuning and Dataset for Defect Detection Models in Photovoltaic Modules Images
Journal of the Korean Solar Energy Society·2025Open Access
Discovery of Alloy Catalysts for Ammonia Decomposition by Machine Learning-Based Prediction of Adsorption Energies
Korean Journal of Metals and Materials·2024· 2 citedOpen Access
Unsupervised and supervised machine learning for feature classification in atomic resolution images
BIO Web of Conferences·2024Open Access
Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy
npj Computational Materials·2023· 28 citedOpen Access
Machine learning filters out efficient electrocatalysts in the massive ternary alloy space for fuel cells
Applied Catalysis B: Environmental·2023· 22 cited
Automatic Identification of Crystal Structures and Interfaces via Artificial-Intelligence-based Electron Microscopy
arXiv (Cornell University)·2023· 4 citedOpen Access
Recovering Microscopic Images in Material Science Documents by Image Inpainting
Applied Sciences·2023· 2 citedOpen Access
Data for "Automatic Identification of Crystal Structures and Interfaces via Artificial-Intelligence-based Electron Microscopy"
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
High-throughput design of bimetallic core–shell catalysts for the electrochemical nitrogen reduction reaction
Journal of Materials Chemistry A·2023· 6 cited
High-throughput Screening Computation for Discovery of Porous Zeolites for Hydrogen Storage
Korean Journal of Metals and Materials·2022· 5 citedOpen Access
High-throughput computational-experimental screening protocol for the discovery of bimetallic catalysts
npj Computational Materials·2021· 1 citedOpen Access
Accelerated mapping of electronic density of states patterns of metallic nanoparticles via machine-learning
Scientific Reports·2021· 26 citedOpen Access
Artificial-Intelligence-Driven Characterization of Crystallographic Interfaces from Electron Microscopy
Bulletin of the American Physical Society·2021
Solid-solution alloying of immiscible Pt and Au boosts catalytic performance for H2O2 direct synthesis
Acta Materialia·2020· 20 cited
High-Throughput Computational-Experimental Screening Protocol for the\n Discovery of Bimetallic Catalysts
arXiv (Cornell University)·2020Open Access
Accelerated Mapping of Electronic Density of States Patterns of Metallic Nanoparticles via Machine-Learning
arXiv (Cornell University)·2020Open Access
Solid-Solution Alloying of Immiscible Pt and AU Boosts Catalytic Performance for H2o2 Direct Synthesis
SSRN Electronic Journal·2020Open Access
Artificial Intelligence to Accelerate the Discovery of N<sub>2</sub> Electroreduction Catalysts
Chemistry of Materials·2019· 98 cited
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Frequent Co-authors

Sang Soo Han(28), Dong-Hun Kim(18), Hyun Suk Jung(6), Hong Woo Lee(6), Hyobin Nam(6), Seung Yong Lee(6), Kwang‐Ryeol Lee(5), Kwan‐Young Lee(5), Andreas Leitherer(5), Christian H. Liebscher(5), Luca M. Ghiringhelli(5), Sung Jin Pai(4), Hyuck Mo Lee(4), Kihoon Bang(4), Min Cheol Kim(4), Seungchul Kim(3), K. P. S. S. Hembram(2), Heechae Choi(2), Dongbin Shin(2), Noejung Park(2)