Publications
12
Citations
272
Est. group size
—
Recurring co-author estimate
Active years
9
Publishing since 2018
Eun Hyun Cho's research combines computational modeling and machine learning with experimental chemistry to design and understand functional materials, including porous materials for gas capture, catalysts for sugar conversion reactions, and materials for OLED (organic light-emitting diode) displays. Much of the work involves developing computational tools and simulations to predict material properties before or alongside laboratory testing.
Publication output has been modest and fairly steady over the past decade, with a small cluster of papers in 2019-2020, a quiet period around 2023, and a recent uptick in 2024-2026.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Molcano: Molecular Language for Chemical Assembly Notation
npj Computational Materials · 2026
- Physics-informed machine learning for spectrum prediction in phosphorescent OLEDs
npj Computational Materials · 2026
- Evaluating the Activity of Heterogeneous Tertiary Amine Catalysts for Glucose Isomerization to Fructose by Tuning Catalyst, Support, and Reaction Conditions
Industrial & Engineering Chemistry Research · 2025
- Simulation-Assisted Deep Learning Techniques for Commercially Applicable OLED Phosphorescent Materials
Chemistry of Materials · 2024
- Investigating the impact of micropore volume of aminosilica functionalized SBA-15 on catalytic activity for amine-catalyzed reactions
Journal of Catalysis · 2022
- Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties
The Journal of Physical Chemistry Letters · 2021
- Machine Learning-Aided Computational Study of Metal–Organic Frameworks for Sour Gas Sweetening
The Journal of Physical Chemistry C · 2020
- Efficient and Accurate Charge Assignments via a Multilayer Connectivity-Based Atom Contribution (m-CBAC) Approach
The Journal of Physical Chemistry C · 2020
- Tuning molecular structure of tertiary amine catalysts for glucose isomerization
Journal of Catalysis · 2019
- Computational discovery of nanoporous materials for energy- and environment-related applications
Molecular Simulation · 2019
- Electrostatic Potential Optimized Molecular Models for Molecular Simulations: CO, CO<sub>2</sub>, COS, H<sub>2</sub>S, N<sub>2</sub>, N<sub>2</sub>O, and SO<sub>2</sub>
Journal of Chemical Theory and Computation · 2019
- Systematic molecular model development with reliable charge distributions for gaseous adsorption in nanoporous materials
Journal of Materials Chemistry A · 2018
- The Journal of Physical Chemistry C×2
- Journal of Catalysis×2
- npj Computational Materials×2
- The Journal of Physical Chemistry Letters×1
- Molecular Simulation×1
- Manish Maurya
Chemistry · The Ohio State University
- Li‐Chiang Lin
Chemistry · The Ohio State University
- Jiehye Shin
Chemistry · The Ohio State University
- Haiyan Wang
Chemistry · Purdue University West Lafayette
- Ping Zhang
Chemistry · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
Claim or correct this profile