Cartesian atomic moment machine learning interatomic potentials
Fuente:
arXiv
Saved in:
| Main Authors: | Wen, Mingjian, Huang, Wei-Fan, Dai, Jin, Adhikari, Santosh |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Uncertainty Quantification and Propagation in Atomistic Machine Learning
by: Dai, Jin, et al.
Published: (2024)
by: Dai, Jin, et al.
Published: (2024)
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)
Graph atomic cluster expansion for foundational machine learning interatomic potentials
by: Lysogorskiy, Yury, et al.
Published: (2025)
by: Lysogorskiy, Yury, 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)
Benchmarking phonon anharmonicity in machine learning interatomic potentials
by: Bandi, Sasaank, et al.
Published: (2024)
by: Bandi, Sasaank, et al.
Published: (2024)
Platonic representation of foundation machine learning interatomic potentials
by: Li, Zhenzhu, et al.
Published: (2025)
by: Li, Zhenzhu, 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)
Cross-functional transferability in universal machine learning interatomic potentials
by: Huang, Xu, et al.
Published: (2025)
by: Huang, Xu, 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)
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)
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)
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)
Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)
by: Chorna, Sofiia, 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)
Strain-Dependent Ionic Transport in Li3YCl6 Solid Electrolytes
by: Huang, Wei-Fan, et al.
Published: (2026)
by: Huang, Wei-Fan, et al.
Published: (2026)
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)
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)
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)
Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials
by: Georgaras, Johnathan D., et al.
Published: (2025)
by: Georgaras, Johnathan D., 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)
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)
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)
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)
Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
by: Kurniawan, Yonatan, et al.
Published: (2025)
by: Kurniawan, Yonatan, et al.
Published: (2025)
Cartesian atomic cluster expansion for machine learning interatomic potentials
by: Cheng, Bingqing
Published: (2024)
by: Cheng, Bingqing
Published: (2024)
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)
Studies of Ni-Cr complexation in FLiBe molten salt using machine learning interatomic potentials
by: Attarian, Siamak, et al.
Published: (2024)
by: Attarian, Siamak, et al.
Published: (2024)
Fast and accurate Fe-H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms
by: Ito, Kazuma
Published: (2025)
by: Ito, Kazuma
Published: (2025)
AtomProNet: Data flow to and from machine learning interatomic potentials in materials science
by: Galib, Musanna, et al.
Published: (2025)
by: Galib, Musanna, 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)
by: Kim, Jisu, et al.
Published: (2025)
Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials
by: Zhong, Peichen, et al.
Published: (2025)
by: Zhong, Peichen, et al.
Published: (2025)
Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
by: Nam, Juno, et al.
Published: (2024)
by: Nam, Juno, et al.
Published: (2024)
How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
by: Kempen, Luuk H. E., et al.
Published: (2025)
by: Kempen, Luuk H. E., 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)
Beam induced heating in electron microscopy modeled with machine learning interatomic potentials
by: Valencia, Cuauhtemoc Nuñez, et al.
Published: (2023)
by: Valencia, Cuauhtemoc Nuñez, et al.
Published: (2023)
Similar Items
-
Uncertainty Quantification and Propagation in Atomistic Machine Learning
by: Dai, Jin, et al.
Published: (2024) -
KLIFF: A framework to develop physics-based and machine learning interatomic potentials
by: Wen, Mingjian, et al.
Published: (2021) -
Graph atomic cluster expansion for foundational machine learning interatomic potentials
by: Lysogorskiy, Yury, 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) -
Benchmarking phonon anharmonicity in machine learning interatomic potentials
by: Bandi, Sasaank, et al.
Published: (2024)