Machine-learning interatomic potential for AlN for epitaxial simulation
Fuente:
arXiv
Saved in:
| Main Authors: | Taormina, Nicholas, Bilgili, Emir, Gibson, Jason, Hennig, Richard, Phillpot, Simon, Chen, Youping |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Effect of Misfit and Threading Dislocations on Surface Energies of PbTe-PbSe Interfaces
by: Bilgili, Emir, et al.
Published: (2025)
by: Bilgili, Emir, et al.
Published: (2025)
Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al and Ni
by: Fellman, Aslak, et al.
Published: (2024)
by: Fellman, Aslak, et al.
Published: (2024)
Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025)
by: Zhong, Peichen, et al.
Published: (2025)
Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
by: Gumber, Shriya, et al.
Published: (2025)
by: Gumber, Shriya, et al.
Published: (2025)
Ultra-fast interpretable machine-learning potentials
by: Xie, Stephen R., et al.
Published: (2021)
by: Xie, Stephen R., et al.
Published: (2021)
Shubnikov-de Haas oscillations in coherently strained AlN/GaN/AlN quantum wells on bulk AlN substrates
by: Chen, Yu-Hsin, et al.
Published: (2025)
by: Chen, Yu-Hsin, et al.
Published: (2025)
Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability
by: Mukhamedov, Boburjon, et al.
Published: (2024)
by: Mukhamedov, Boburjon, et al.
Published: (2024)
Screening of material defects using universal machine-learning interatomic potentials
by: Berger, Ethan, et al.
Published: (2025)
by: Berger, Ethan, et al.
Published: (2025)
Nine-element machine-learned interatomic potentials for multiphase refractory alloys
by: Byggmästar, Jesper, et al.
Published: (2026)
by: Byggmästar, Jesper, et al.
Published: (2026)
Machine-learning potential for phonon transport in AlN with defects in multiple charge states
by: Dou, Ying, et al.
Published: (2024)
by: Dou, Ying, et al.
Published: (2024)
Temperature Dependent Characteristics of Quasi-vertical AlN Schottky Diodes on Bulk AlN Substrate
by: Hamid, Md Abdul, et al.
Published: (2026)
by: Hamid, Md Abdul, et al.
Published: (2026)
Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning
by: Klimanova, Olga, et al.
Published: (2024)
by: Klimanova, Olga, et al.
Published: (2024)
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)
Accurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystals
by: Della Pia, Flaviano, et al.
Published: (2025)
by: Della Pia, Flaviano, et al.
Published: (2025)
Development of TiN/AlN-based superconducting qubit components
by: Schoof, Benedikt, et al.
Published: (2024)
by: Schoof, Benedikt, et al.
Published: (2024)
Machine-Learned Atomic Cluster Expansion Potentials for Fast and Quantum-Accurate Thermal Simulations of Wurtzite AlN
by: Yang, Guang, et al.
Published: (2023)
by: Yang, Guang, et al.
Published: (2023)
Rock-salt ScN(113) layers grown on AlN$(11\bar{2}2)$ by plasma-assisted molecular beam epitaxy
by: Dinh, Duc V., et al.
Published: (2025)
by: Dinh, Duc V., et al.
Published: (2025)
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)
Fine-tuning of universal machine-learning interatomic potentials for 2D high-entropy alloys
by: Zhou, Chun, et al.
Published: (2026)
by: Zhou, Chun, et al.
Published: (2026)
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
by: Zaverkin, Viktor, et al.
Published: (2023)
by: Zaverkin, Viktor, et al.
Published: (2023)
Structural and optical properties of self-assembled AlN nanowires grown on SiO2/Si substrates by molecular beam epitaxy
by: Gačević, Ž., et al.
Published: (2024)
by: Gačević, Ž., et al.
Published: (2024)
Elastic modeling and total energy calculations of the structural characteristics of "free-standing",periodic, pseudomorphic GaN/AlN superlattices
by: Karakostas, Th., et al.
Published: (2025)
by: Karakostas, Th., et al.
Published: (2025)
Combining graph deep learning and London dispersion interatomic potentials: A case study on pnictogen chalcohalides
by: Kılıç, Çetin, et al.
Published: (2024)
by: Kılıç, Çetin, et al.
Published: (2024)
XHEMTs on Ultrawide Bandgap Single-Crystal AlN Substrates
by: Kim, Eungkyun, et al.
Published: (2025)
by: Kim, Eungkyun, et al.
Published: (2025)
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)
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
by: Kuner, Matthew C., et al.
Published: (2025)
by: Kuner, Matthew C., et al.
Published: (2025)
Strain distribution in GaN/AlN superlattices grown on AlN/sapphire templates: comparison of X-ray diffraction and photoluminescence studies
by: Wierzbicka, Aleksandra, et al.
Published: (2025)
by: Wierzbicka, Aleksandra, et al.
Published: (2025)
MAD-SURF: a machine learning interatomic potential for molecular adsorption on coinage metal surfaces
by: Lastre, Manuel González, et al.
Published: (2026)
by: Lastre, Manuel González, et al.
Published: (2026)
FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
by: Kang, Hanwen, et al.
Published: (2025)
by: Kang, Hanwen, et al.
Published: (2025)
Long-range electrostatics for machine learning interatomic potentials is easier than we thought
by: Kim, Dongjin, et al.
Published: (2025)
by: Kim, Dongjin, et al.
Published: (2025)
Wurtzite AlScN/AlN Superlattice Ferroelectrics Enable Endurance Beyond 1010 Cycles
by: Wang, Ruiqing, et al.
Published: (2025)
by: Wang, Ruiqing, et al.
Published: (2025)
Molecular Beam Homoepitaxy of N-polar AlN on bulk AlN substrates
by: Singhal, Jashan, et al.
Published: (2022)
by: Singhal, Jashan, et al.
Published: (2022)
AlN Nanowire Based Vertically Integrated Piezoelectric Nanogenerators
by: Buatip, N., et al.
Published: (2024)
by: Buatip, N., et al.
Published: (2024)
When More Data Hurts: Optimizing Data Coverage While Mitigating Diversity Induced Underfitting in an Ultra-Fast Machine-Learned Potential
by: Gibson, Jason B., et al.
Published: (2024)
by: Gibson, Jason B., et al.
Published: (2024)
Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost
by: Baghishov, Ilgar, et al.
Published: (2025)
by: Baghishov, Ilgar, et al.
Published: (2025)
Growth kinetics and substrate stability during high-temperature molecular beam epitaxy of AlN nanowires
by: John, Philipp, et al.
Published: (2023)
by: John, Philipp, et al.
Published: (2023)
Metalorganic Chemical Vapor Deposition of AlScN Thin Films and AlScN/AlN/GaN Heterostructures
by: Vangipuram, Vijay Gopal Thirupakuzi, et al.
Published: (2025)
by: Vangipuram, Vijay Gopal Thirupakuzi, 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)
Machine learning interatomic potentials for solid-state precipitation
by: Piersante, Lorenzo, et al.
Published: (2026)
by: Piersante, Lorenzo, et al.
Published: (2026)
Nitrogen-polar growth of AlN on vicinal (0001) sapphire by MOVPE
by: Pampili, Pietro, et al.
Published: (2024)
by: Pampili, Pietro, et al.
Published: (2024)
Similar Items
-
Effect of Misfit and Threading Dislocations on Surface Energies of PbTe-PbSe Interfaces
by: Bilgili, Emir, et al.
Published: (2025) -
Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al and Ni
by: Fellman, Aslak, et al.
Published: (2024) -
Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025) -
Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
by: Gumber, Shriya, et al.
Published: (2025) -
Ultra-fast interpretable machine-learning potentials
by: Xie, Stephen R., et al.
Published: (2021)