Chang‐Ho Hong

Korea Advanced Institute of Science and Technology — South Korea

Materials Science · Physical Sciences

12h-index632citations56works3.12yr avg
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Research Topics

Machine Learning in Materials Science(11), X-ray Diffraction in Crystallography(11), Composite Structure Analysis and Optimization(6), Advanced Semiconductor Detectors and Materials(6), Semiconductor materials and interfaces(6)

Publications55 total

Settlement Behavior of Bacterium-Embedded Soft Clay Sediment: Sedimentation and Low-Stress Consolidation Experiment
Journal of Geotechnical and Geoenvironmental Engineering·2026
A Computational Study for Screening High-Selectivity Inhibitors in Area-Selective Atomic Layer Deposition on Amorphous Surfaces
arXiv (Cornell University)·2025Open Access
Unveiling Defect Motifs in Amorphous GeSe Using Machine Learning Interatomic Potentials
ACS Applied Materials & Interfaces·2025· 1 cited
Etching-to-deposition transition in SiO2/Si <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si206.svg" display="inline" id="d1e1145"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msub> </mml:math> N <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si207.svg" display="inline" id="d1e1153"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>4</mml:mn> </mml:mrow> </mml:msub> </mml:math> using CH <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si110.svg" display="inline" id="d1e1161"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> </mml:msub> </mml:math> F <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si111.svg" display="inline" id="d1e1170"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mi>y</mml:mi> </mml:mrow> </mml:msub> </mml:math> ion-based plasma etching: An atomistic study with neural network potentials
Applied Surface Science·2025
Etching-to-deposition transition in SiO$_2$/Si$_3$N$_4$ using CH$_x$F$_y$ ion-based plasma etching: An atomistic study with neural network potentials
arXiv (Cornell University)·2025Open Access
Laboratory evidences on accelerated fatigue failure in brittle granitic rock by coupled thermal and mechanical cyclic loading: Acoustic emission monitoring and implication to underground hydrogen storage
Geomechanics for Energy and the Environment·2025· 6 citedOpen Access
Etching-to-Deposition Transition in Sio2/Si3n4 Using Chxfy Ion-Based Plasma Etching: An Atomistic Study with Neural Network Potentials
SSRN Electronic Journal·2025Open Access
Atomistic Simulation of HF Etching Process of Amorphous Si<sub>3</sub>N<sub>4</sub> Using Machine Learning Potential
ACS Applied Materials & Interfaces·2024· 18 cited
Applications and training sets of machine learning potentials
Science and Technology of Advanced Materials Methods·2023· 12 citedOpen Access
Stability and Equilibrium Structures of Unknown Ternary Metal Oxides Explored by Machine-Learned Potentials
Journal of the American Chemical Society·2023· 24 cited
Design of stress level for accelerated life test of HLW repository sensor
2023Open Access
Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials
npj Computational Materials·2022· 34 citedOpen Access
Ensemble Learning Approach for the Prediction of Quantitative Rock Damage Using Various Acoustic Emission Parameters
Applied Sciences·2021· 24 citedOpen Access
Data-Driven Modelling of Damage Prediction of Granite Using Acoustic Emission Parameters in Nuclear Waste Repository
Journal of the Nuclear Fuel Cycle and Waste Technology(JNFCWT)·2021Open Access
Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials
Figshare·2021· 1 citedOpen Access
AI Automation Smart Access Management System using Personal Authentication and Heat Detector
Proceedings of the Korean Institute of Information and Commucation Sciences Conference·2021
A band-gap database for semiconducting inorganic materials calculated with hybrid functional
Figshare·2021Open Access
Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials
Figshare·2021Open Access
Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials
Figshare·2021Open Access
Training machine-learning potentials for crystal structure prediction using disordered structures
Physical review. B./Physical review. B·2020· 35 citedOpen Access
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Frequent Co-authors

Seungwu Han(21), Wonseok Jeong(13), Youngchae Yoon(11), Hyungmin An(11), Yong Youn(9), Inderjit Chopra(8), Miso Lee(8), Dongsun Yoo(7), Youngho Kang(7), Sangtae Kim(6), Sungwoo Kang(6), Seungwoo Hwang(6), Dongheon Lee(5), Gye-Chun Cho(5), Jisu Jung(4), Sangmin Oh(4), Jae-hyeon Ko(4), Dongyean Oh(4), Dong-Heon Lee(3), Jin-Seop Kim(3)