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Author

Taehyun Park

Institution: Soongsil University
ORCID: 0000-0002-1294-9577
Observed ORCID: 0000-0002-1294-9577
OpenAlex ID: A5100763299
Toward fast and accurate machine learning interatomic potentials for atomic layer deposition precursors
2024 · Seungpyo Kang, Joonchul Kim, et al. · Materials Today Advances · 17
Rapid discovery of promising materials via active learning with multi-objective optimization
2023 · Taehyun Park, Eunsong Kim, et al. · Materials Today Communications · 16
Enhancing structural stability of Prussian white cathodes via dopant effects: A computational study
2025 · Jinyoung Jeong, Seungpyo Kang, et al. · Materials Today Energy · 10
Design strategies for fast-charging multiphase Na-ion layered cathodes: Dopant selection via computational high-throughput screening
2025 · Taehyun Park, Juo Kim, et al. · Journal of Energy Chemistry · 9
Enhancing predictions of experimental band gap using machine learning and knowledge transfer
2024 · Taeseo Ko, Taehyun Park, et al. · Materials Today Communications · 8
Predicting mechanical properties of newly generated two-dimensional materials with minimum uncertainty
2023 · Inhyo Lee, Joonchul Kim, et al. · Materials Today Advances · 7
Cation-Disordered High-Entropy Garnet Structures as Solid-State Electrolytes for All-Solid-State Batteries: Machine Learning-Driven Discovery
2025 · Jiwon Sun, JunHo Song, et al. · ACS Applied Materials & Interfaces · 6
Integrating Data Mining and Natural Language Processing to Construct a Melting Point Database for Organometallic Compounds
2024 · Jinyoung Jeong, Taehyun Park, et al. · Journal of Chemical Information and Modeling · 6
Revealing the role of dopants to suppress phase transition in biphasic layered transition metal oxide cathodes for sodium-ion batteries
2025 · Taehyun Park, Jinyoung Jeong, et al. · Journal of Power Sources · 4
Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design
2024 · Myeonghun Lee, Taehyun Park, et al. · Journal of Chemical Information and Modeling · 3
Evaluating Machine Learning Interatomic Potentials for Accurate and Scalable Modeling of Organometallic Precursors
2025 · Seungpyo Kang, JunHo Song, et al. · ACS Applied Materials & Interfaces · 2
Interpretable machine learning boosting the discovery of targeted organometallic compounds with optimal bandgap
2024 · Taehyun Park, JunHo Song, et al. · Materials Today Advances · 2
Data-driven curation of organometallic precursor boiling points via natural language processing
2025 · Jungjae Hwang, Taehyun Park, et al. · Materials & Design · 1
High-throughput computational screening of dopants for improved structural stability in overlithiated layered oxide cathodes
2024 · Seungpyo Kang, Jinyoung Jeong, et al. · Journal of Power Sources · 1
Works count: 14
Citation count: 92
H-index: 6
I10-index: 3
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