Incorporating Gibbs free energy into interatomic potential fitting
Fuente:
arXiv
Saved in:
| Main Authors: | Wei, Liangrui, Sun, Yang |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials
by: Parackal, Abhijith S, et al.
Published: (2026)
by: Parackal, Abhijith S, et al.
Published: (2026)
Cartesian atomic moment machine learning interatomic potentials
by: Wen, Mingjian, et al.
Published: (2024)
by: Wen, Mingjian, et al.
Published: (2024)
Efficient first-principles approach to Gibbs free energy with thermal expansion
by: Hashimoto, Kota, et al.
Published: (2024)
by: Hashimoto, Kota, et al.
Published: (2024)
A KIM-compliant potfit for fitting sloppy interatomic potentials: Application to the EDIP model for silicon
by: Wen, Mingjian, et al.
Published: (2016)
by: Wen, Mingjian, et al.
Published: (2016)
Machine learning interatomic potentials for solid-state precipitation
by: Piersante, Lorenzo, et al.
Published: (2026)
by: Piersante, Lorenzo, 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)
Benchmarking phonon anharmonicity in machine learning interatomic potentials
by: Bandi, Sasaank, et al.
Published: (2024)
by: Bandi, Sasaank, et al.
Published: (2024)
The Fe-Ni phase diagram and the Earth's inner core structure
by: Wei, Liangrui, et al.
Published: (2025)
by: Wei, Liangrui, 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)
Systematic assessment of various universal machine-learning interatomic potentials
by: Yu, Haochen, et al.
Published: (2024)
by: Yu, Haochen, et al.
Published: (2024)
Assessing foundational atomistic models for iron alloys under Earth's core conditions
by: Wan, Tianqi, et al.
Published: (2026)
by: Wan, Tianqi, et al.
Published: (2026)
Leveraging neural network interatomic potentials for a foundation model of chemistry
by: Kim, So Yeon, et al.
Published: (2025)
by: Kim, So Yeon, et al.
Published: (2025)
Graph atomic cluster expansion for foundational machine learning interatomic potentials
by: Lysogorskiy, Yury, et al.
Published: (2025)
by: Lysogorskiy, Yury, et al.
Published: (2025)
Machine learning interatomic potential for predicting the thermal properties of uranium nitride
by: Chen, Beihan, et al.
Published: (2025)
by: Chen, Beihan, 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)
Facet: highly efficient E(3)-equivariant networks for interatomic potentials
by: Miklaucic, Nicholas, et al.
Published: (2025)
by: Miklaucic, Nicholas, et al.
Published: (2025)
Machine learning Landau free energy potentials
by: Pulzone, Mauro, et al.
Published: (2025)
by: Pulzone, Mauro, et al.
Published: (2025)
Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)
by: Chorna, Sofiia, et al.
Published: (2025)
Observation of spin-free interatomic orbital angular momentum in a chiral crystal
by: Oh, Dongjin, et al.
Published: (2026)
by: Oh, Dongjin, et al.
Published: (2026)
Triplet Envelope Functions for increasing machine learning interatomic potential efficiency and stability
by: Annevelink, Emil, et al.
Published: (2026)
by: Annevelink, Emil, et al.
Published: (2026)
KLIFF: A framework to develop physics-based and machine learning interatomic potentials
by: Wen, Mingjian, et al.
Published: (2021)
by: Wen, Mingjian, et al.
Published: (2021)
Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning
by: Radova, Mariia, et al.
Published: (2025)
by: Radova, Mariia, et al.
Published: (2025)
Fast machine learned $α$-Fe-H interatomic potential for hydrogen embrittlement
by: Makkonen, Eetu, et al.
Published: (2025)
by: Makkonen, Eetu, et al.
Published: (2025)
Atomic cluster expansion interatomic potential for defects and thermodynamics of Cu-W system
by: Pan, Jiahao, et al.
Published: (2024)
by: Pan, Jiahao, et al.
Published: (2024)
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)
Machine-learning interatomic potential for AlN for epitaxial simulation
by: Taormina, Nicholas, et al.
Published: (2025)
by: Taormina, Nicholas, 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)
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)
Radial gradient of superionic hydrogen in Earth's inner core
by: Wu, Zepeng, et al.
Published: (2026)
by: Wu, Zepeng, et al.
Published: (2026)
A practical guide to machine learning interatomic potentials -- Status and future
by: Jacobs, Ryan, et al.
Published: (2025)
by: Jacobs, Ryan, et al.
Published: (2025)
Dislocations and plasticity of KTaO$_3$ perovskite modeled with a new interatomic potential
by: Hirel, Pierre, et al.
Published: (2025)
by: Hirel, Pierre, et al.
Published: (2025)
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)
Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium
by: Fletcher, Vincent G., et al.
Published: (2025)
by: Fletcher, Vincent G., et al.
Published: (2025)
Minimalist machine-learned interatomic potentials can predict complex structural behaviors accurately
by: Robredo-Magro, Iñigo, et al.
Published: (2025)
by: Robredo-Magro, Iñigo, et al.
Published: (2025)
Development of a magnetic interatomic potential for cubic anti-ferromagnets: the case of NiO
by: Korniienko, Ievgeniia, et al.
Published: (2025)
by: Korniienko, Ievgeniia, et al.
Published: (2025)
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)
Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials
by: Subramanyam, Aparna P. A., et al.
Published: (2024)
by: Subramanyam, Aparna P. A., et al.
Published: (2024)
Thermal boundary conductance of metal diamond interfaces predicted by machine learning interatomic potentials
by: Adnan, Khalid Zobaid, et al.
Published: (2024)
by: Adnan, Khalid Zobaid, et al.
Published: (2024)
An AI-ready fine-tuning framework for accurate machine-learning interatomic potentials in solid-solid battery interfaces
by: Liu, Xiaoqing, et al.
Published: (2026)
by: Liu, Xiaoqing, et al.
Published: (2026)
Similar Items
-
Exploring the energy landscape of aluminas through machine learning interatomic potential
by: Zhang, Lei, et al.
Published: (2024) -
Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials
by: Parackal, Abhijith S, et al.
Published: (2026) -
Cartesian atomic moment machine learning interatomic potentials
by: Wen, Mingjian, et al.
Published: (2024) -
Efficient first-principles approach to Gibbs free energy with thermal expansion
by: Hashimoto, Kota, et al.
Published: (2024) -
A KIM-compliant potfit for fitting sloppy interatomic potentials: Application to the EDIP model for silicon
by: Wen, Mingjian, et al.
Published: (2016)