Imagen de Portada

Atomistic modeling of uranium monocarbide with a machine learning interatomic potential

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Alzate-Vargas, Lorena, Subedi, Kashi N., Tutchton, Roxanne M., Cooper, Michael W. D., Gibson, Tammie, Messerly, Richard A.
Formato:	Preprint
Publicado:	2025
Materias:	Materials Science
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Toward machine learning interatomic potentials for modeling uranium mononitride
por: Alzate-Vargas, Lorena, et al.
Publicado: (2024)

Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
por: Gumber, Shriya, et al.
Publicado: (2025)

Machine learning interatomic potential for predicting the thermal properties of uranium nitride
por: Chen, Beihan, et al.
Publicado: (2025)

Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance
por: Cho, Hyunsung, et al.
Publicado: (2025)

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

Platonic representation of foundation machine learning interatomic potentials
por: Li, Zhenzhu, et al.
Publicado: (2025)

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

Benchmarking phonon anharmonicity in machine learning interatomic potentials
por: Bandi, Sasaank, et al.
Publicado: (2024)

Machine-learning interatomic potential for AlN for epitaxial simulation
por: Taormina, Nicholas, et al.
Publicado: (2025)

Beam induced heating in electron microscopy modeled with machine learning interatomic potentials
por: Valencia, Cuauhtemoc Nuñez, et al.
Publicado: (2023)

Gaunt and Breit Two-electron contributions to Mean-field Transformations and Fine Structure Splitting
por: Murg, Luca, et al.
Publicado: (2025)

Cross-functional transferability in universal machine learning interatomic potentials
por: Huang, Xu, et al.
Publicado: (2025)

Systematic assessment of various universal machine-learning interatomic potentials
por: Yu, Haochen, et al.
Publicado: (2024)

Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials
por: Georgaras, Johnathan D., et al.
Publicado: (2025)

Systematic assessment of various universal machine‐learning interatomic potentials
por: Haochen Yu, et al.
Publicado: (2024)

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
por: Noordhoek, Kyle, et al.
Publicado: (2023)

Exploring the energy landscape of aluminas through machine learning interatomic potential
por: Zhang, Lei, et al.
Publicado: (2024)

Graph atomic cluster expansion for foundational machine learning interatomic potentials
por: Lysogorskiy, Yury, et al.
Publicado: (2025)

Accurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystals
por: Della Pia, Flaviano, et al.
Publicado: (2025)

Comparing the latent features of universal machine-learning interatomic potentials
por: Chorna, Sofiia, et al.
Publicado: (2025)

Screening of material defects using universal machine-learning interatomic potentials
por: Berger, Ethan, et al.
Publicado: (2025)

Nine-element machine-learned interatomic potentials for multiphase refractory alloys
por: Byggmästar, Jesper, et al.
Publicado: (2026)

A practical guide to machine learning interatomic potentials -- Status and future
por: Jacobs, Ryan, et al.
Publicado: (2025)

KLIFF: A framework to develop physics-based and machine learning interatomic potentials
por: Wen, Mingjian, et al.
Publicado: (2021)

Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials
por: Parackal, Abhijith S, et al.
Publicado: (2026)

Triplet Envelope Functions for increasing machine learning interatomic potential efficiency and stability
por: Annevelink, Emil, et al.
Publicado: (2026)

Fast machine learned $α$-Fe-H interatomic potential for hydrogen embrittlement
por: Makkonen, Eetu, et al.
Publicado: (2025)

Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
por: Nam, Juno, et al.
Publicado: (2024)

How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
por: Kempen, Luuk H. E., et al.
Publicado: (2025)

Machine learning method to determine concentrations of structural defects in irradiated materials
por: Johnson, Landon, et al.
Publicado: (2025)

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

Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium
por: Fletcher, Vincent G., et al.
Publicado: (2025)

Minimalist machine-learned interatomic potentials can predict complex structural behaviors accurately
por: Robredo-Magro, Iñigo, et al.
Publicado: (2025)

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

Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials
por: Subramanyam, Aparna P. A., et al.
Publicado: (2024)

Thermal boundary conductance of metal diamond interfaces predicted by machine learning interatomic potentials
por: Adnan, Khalid Zobaid, et al.
Publicado: (2024)

MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
por: Kuner, Matthew C., et al.
Publicado: (2025)

The structure and migration of twin boundaries in tetragonal $β$-Sn: an application of machine learning based interatomic potentials
por: Chesser, Ian, et al.
Publicado: (2025)

A collinear-spin machine learned interatomic potential for Fe$_{7}$Cr$_{2}$Ni alloy
por: Shenoy, Lakshmi, et al.
Publicado: (2023)

Accelerating point defect photo-emission calculations with machine learning interatomic potentials
por: Sharma, Kartikeya, et al.
Publicado: (2025)