Cover Image

Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cho, Hyunsung, Moon, Minseok, Kim, Jaehoon, Koh, Eunkyung, Kim, Hyeon-Deuk, Kim, Rokyeon, Park, Gyehyun, Han, Seungwu, Kang, Youngho
Format:	Preprint
Published:	2025
Subjects:	Materials Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Atomic-Scale Mechanisms of SiO$_2$ Plasma-Enhanced Chemical Vapor Deposition Revealed by Molecular Dynamics with a Machine-Learning Interatomic Potential
by: Kim, Jaehoon, et al.
Published: (2026)

Data-efficient multi-fidelity training for high-fidelity machine learning interatomic potentials
by: Kim, Jaesun, et al.
Published: (2024)

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials
by: Moon, Minseok, et al.
Published: (2025)

Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials
by: Moon, Minseok, et al.
Published: (2025)

Electronic structures of crystalline and amorphous GeSe and GeSbTe compounds using machine learning empirical pseudopotentials
by: Kang, Sungmo, et al.
Published: (2025)

Neural Network-Driven Molecular Insights into Alkaline Wet Etching of GaN: Toward Atomistic Precision in Nanostructure Fabrication
by: Kim, Purun-hanul, et al.
Published: (2025)

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
by: Kim, Jisu, et al.
Published: (2025)

Transferable empirical pseudopotenials from machine learning
by: Kim, Rokyeon, et al.
Published: (2023)

A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations
by: Oh, Sangmin, et al.
Published: (2026)

Application of pretrained universal machine-learning interatomic potential for physicochemical simulation of liquid electrolytes in Li-ion battery
by: Ju, Suyeon, et al.
Published: (2025)

Atomistic Insights into Cu/amorphous-Ta$_x$N Interfacial Adhesion via Machine Learning Interatomic Potentials: Effects of Stoichiometry and Interface Construction
by: Choi, Jeong Min, et al.
Published: (2025)

On the Sum of Reciprocals of Primes
by: Kim, Young Deuk
Published: (2024)

Ordinality and Riemann Hypothesis II
by: Kim, Young Deuk
Published: (2024)

Ordinality and Riemann Hypothesis I
by: Kim, Young Deuk
Published: (2023)

Loss-Optimized Reconfigurable Nonlocal Metasurface-aided Cavity Antenna
by: Cho, Minwoo, et al.
Published: (2026)

Atomistic modeling of uranium monocarbide with a machine learning interatomic potential
by: Alzate-Vargas, Lorena, et al.
Published: (2025)

Scalable Reactive Atomistic Dynamics with GAIA
by: Song, Suhwan, et al.
Published: (2025)

Neural Policy Iteration for Stochastic Optimal Control: A Physics-Informed Approach
by: Kim, Yeongjong, et al.
Published: (2025)

Physics-informed approach for exploratory Hamilton--Jacobi--Bellman equations via policy iterations
by: Kim, Yeongjong, et al.
Published: (2025)

Stabilized neural Hamilton--Jacobi--Bellman solvers: Error analysis and applications in model-based reinforcement learning
by: Kim, Minseok, et al.
Published: (2026)

Long-range electrostatics for machine learning interatomic potentials is easier than we thought
by: Kim, Dongjin, et al.
Published: (2025)

Increasing the p-Selmer rank by twisting
by: Kim, Minseok
Published: (2025)

$p$-twisted Selmer near-companion curves
by: Kim, Minseok
Published: (2025)

85‐1: Invited Paper: A Novel OLED Material Discovery based on AI Technology
by: Hoilim Kim, et al.
Published: (2024)

From Sharp to Blur: Unsupervised Domain Adaptation for 2D Human Pose Estimation Under Extreme Motion Blur Using Event Cameras
by: Kim, Youngho, et al.
Published: (2025)

Software-Defined Cryptography: A Design Feature of Cryptographic Agility
by: Cho, Jihoon, et al.
Published: (2024)

Cartesian atomic moment machine learning interatomic potentials
by: Wen, Mingjian, et al.
Published: (2024)

Density diversity in training data governs thermodynamic transferability of machine learning interatomic potentials
by: Kim, Minwoo, et al.
Published: (2026)

Nonlocal Dual-Band Reconfigurable Intelligent Surfaces for Precise Full-Space Beamforming
by: Kim, Moosung, et al.
Published: (2026)

Engineering Spatial Dispersion to Synthesize Arbitrary Spatial Filters Based on Metagratings
by: Kim, Jinyong, et al.
Published: (2026)

On the size of universal graphs for spanning trees
by: Kim, Jaehoon, et al.
Published: (2025)

Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li$_6$PS$_5$Cl using machine-learning interatomic potentials
by: Ou, Yongliang, et al.
Published: (2024)

Non-split, alternating links bound unique Seifert surfaces in the 4-ball
by: Kim, Seungwon, et al.
Published: (2024)

Cartesian atomic cluster expansion for machine learning interatomic potentials
by: Cheng, Bingqing
Published: (2024)

Unique ID based Trust Scheme for Improved IoV Wireless Sensor Network Security Against Power Controlled Sybil Attacks
by: Kim, Jae-Dong, et al.
Published: (2024)

PID‐Based Freeway Work Zone Merge Control with Traffic State Prediction under Mixed Traffic Flow of Connected Automated Vehicles and Manual Vehicles
by: Sunho Kim, et al.
Published: (2024)

Physics-Informed Neural Networks for Optimal Vaccination Plan in SIR Epidemic Models
by: Kim, Minseok, et al.
Published: (2025)

Knowing when to trust machine-learned interatomic potentials
by: Mehdi, Shams, et al.
Published: (2026)

Discovery of oxide Li-conducting electrolytes in uncharted chemical space via topology-constrained crystal structure prediction
by: Hwang, Seungwoo, et al.
Published: (2025)

Relationship between Health Decision‐Making Roles and Subjective Memory Ability among Older Adults in Rural Community
by: Myonghwa Park, et al.
Published: (2024)