Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials
Fuente:
arXiv
Saved in:
| Main Authors: | Moon, Minseok, Hwang, Seungwoo, Kim, Jaesun, Park, Yutack, Hong, Changho, Han, Seungwu |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials
by: Moon, Minseok, et al.
Published: (2025)
by: Moon, Minseok, et al.
Published: (2025)
Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations
by: Park, Yutack, et al.
Published: (2024)
by: Park, Yutack, et al.
Published: (2024)
Data-efficient multi-fidelity training for high-fidelity machine learning interatomic potentials
by: Kim, Jaesun, et al.
Published: (2024)
by: Kim, Jaesun, et al.
Published: (2024)
An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
by: Kim, Jisu, et al.
Published: (2025)
by: Kim, Jisu, 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)
by: Kang, Sungmo, et al.
Published: (2025)
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)
by: Ju, Suyeon, et al.
Published: (2025)
Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance
by: Cho, Hyunsung, et al.
Published: (2025)
by: Cho, Hyunsung, et al.
Published: (2025)
Screening of material defects using universal machine-learning interatomic potentials
by: Berger, Ethan, et al.
Published: (2025)
by: Berger, Ethan, et al.
Published: (2025)
Accelerating point defect photo-emission calculations with machine learning interatomic potentials
by: Sharma, Kartikeya, et al.
Published: (2025)
by: Sharma, Kartikeya, et al.
Published: (2025)
Unveiling the crystallization kinetics in Ge-rich Ge$_x$Te alloys by large scale simulations with a machine-learned interatomic potential
by: Baratella, Dario, et al.
Published: (2024)
by: Baratella, Dario, et al.
Published: (2024)
Discovery of oxide Li-conducting electrolytes in uncharted chemical space via topology-constrained crystal structure prediction
by: Hwang, Seungwoo, et al.
Published: (2025)
by: Hwang, Seungwoo, et al.
Published: (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
by: An, Hyungmin, et al.
Published: (2025)
by: An, Hyungmin, et al.
Published: (2025)
Efficient moment tensor machine-learning interatomic potential for accurate description of defects in Ni-Al Alloys
by: Wang, Jiantao, et al.
Published: (2024)
by: Wang, Jiantao, et al.
Published: (2024)
Crystallographic defects in Weyl semimetal LaAlGe
by: Kim, Inseo, et al.
Published: (2024)
by: Kim, Inseo, et al.
Published: (2024)
Optimizing Cross-Domain Transfer for Universal Machine Learning Interatomic Potentials
by: Kim, Jaesun, et al.
Published: (2025)
by: Kim, Jaesun, et al.
Published: (2025)
A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations
by: Oh, Sangmin, et al.
Published: (2026)
by: Oh, Sangmin, et al.
Published: (2026)
Platonic representation of foundation machine learning interatomic potentials
by: Li, Zhenzhu, et al.
Published: (2025)
by: Li, Zhenzhu, et al.
Published: (2025)
Cartesian atomic moment machine learning interatomic potentials
by: Wen, Mingjian, et al.
Published: (2024)
by: Wen, Mingjian, et al.
Published: (2024)
Benchmarking phonon anharmonicity in machine learning interatomic potentials
by: Bandi, Sasaank, et al.
Published: (2024)
by: Bandi, Sasaank, et al.
Published: (2024)
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)
by: Kim, Jaehoon, et al.
Published: (2026)
A Computational Study for Screening High-Selectivity Inhibitors in Area-Selective Atomic Layer Deposition on Amorphous Surfaces
by: Kim, Gijin, et al.
Published: (2025)
by: Kim, Gijin, et al.
Published: (2025)
Compositing Effect Leads to Extraordinary Performance in GeSe‐Based Thermoelectrics
by: Min Zhang, et al.
Published: (2025)
by: Min Zhang, et al.
Published: (2025)
GeSe Nanosheets‐Mediated Local Sonocatalytic Immunosuppression of Rheumatoid Arthritis
by: Lingting Zeng, et al.
Published: (2025)
by: Lingting Zeng, et al.
Published: (2025)
Manipulation of metavalent bonding to stabilize metastable phase: A strategy for enhancing zT in GeSe
by: Yilun Huang, et al.
Published: (2024)
by: Yilun Huang, et al.
Published: (2024)
Toward machine learning interatomic potentials for modeling uranium mononitride
by: Alzate-Vargas, Lorena, et al.
Published: (2024)
by: Alzate-Vargas, Lorena, et al.
Published: (2024)
Systematic assessment of various universal machine-learning interatomic potentials
by: Yu, Haochen, et al.
Published: (2024)
by: Yu, Haochen, et al.
Published: (2024)
Are diffusion models ready for materials discovery in unexplored chemical space?
by: Kim, Sanghyun, et al.
Published: (2025)
by: Kim, Sanghyun, et al.
Published: (2025)
High Photovoltaic Efficiency in Bulk-Stacked One-Dimensional GeSe$_{2}$ van der Waals Crystal
by: Kang, Seoung-Hun, et al.
Published: (2026)
by: Kang, Seoung-Hun, et al.
Published: (2026)
Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
by: Shuang, Fei, et al.
Published: (2025)
by: Shuang, Fei, et al.
Published: (2025)
Polarization Signal Amplification of 2D GeSe‐Based Polarization‐Sensitive Photodetectors
by: Kexin He, et al.
Published: (2025)
by: Kexin He, et al.
Published: (2025)
Cross-functional transferability in universal machine learning interatomic potentials
by: Huang, Xu, et al.
Published: (2025)
by: Huang, Xu, et al.
Published: (2025)
Systematic assessment of various universal machine‐learning interatomic potentials
by: Haochen Yu, et al.
Published: (2024)
by: Haochen Yu, et al.
Published: (2024)
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
by: Noordhoek, Kyle, et al.
Published: (2023)
by: Noordhoek, Kyle, et al.
Published: (2023)
Exploring the energy landscape of aluminas through machine learning interatomic potential
by: Zhang, Lei, et al.
Published: (2024)
by: Zhang, Lei, et al.
Published: (2024)
Graph atomic cluster expansion for foundational machine learning interatomic potentials
by: Lysogorskiy, Yury, et al.
Published: (2025)
by: Lysogorskiy, Yury, et al.
Published: (2025)
Atomistic modeling of uranium monocarbide with a machine learning interatomic potential
by: Alzate-Vargas, Lorena, et al.
Published: (2025)
by: Alzate-Vargas, Lorena, et al.
Published: (2025)
Unveiling the amorphous ice layer during premelting using AFM integrating machine learning
by: Tang, Binze, et al.
Published: (2025)
by: Tang, Binze, et al.
Published: (2025)
On phase separation and crystallization of Ge-rich GeSbTe alloys from atomistic simulations with a machine learning interatomic potential
by: Kheir, Omar Abou El, et al.
Published: (2026)
by: Kheir, Omar Abou El, et al.
Published: (2026)
Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead
by: Hellyar, Tom, et al.
Published: (2026)
by: Hellyar, Tom, et al.
Published: (2026)
Structure-property relations of silicon oxycarbides studied using a machine learning interatomic potential
by: Leimeroth, Niklas, et al.
Published: (2024)
by: Leimeroth, Niklas, et al.
Published: (2024)
Similar Items
-
Unveiling defect motifs in amorphous GeSe using machine learning interatomic potentials
by: Moon, Minseok, et al.
Published: (2025) -
Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations
by: Park, Yutack, et al.
Published: (2024) -
Data-efficient multi-fidelity training for high-fidelity machine learning interatomic potentials
by: Kim, Jaesun, et al.
Published: (2024) -
An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
by: Kim, Jisu, 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)