Copertina

Toward machine learning interatomic potentials for modeling uranium mononitride

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Alzate-Vargas, Lorena, Subedi, Kashi N., Lubbers, Nicholas, Cooper, Michael W. D, Tutchton, Roxanne M., Gibson, Tammie, Messerly, Richard A.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Materials Science
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Atomistic modeling of uranium monocarbide with a machine learning interatomic potential
di: Alzate-Vargas, Lorena, et al.
Pubblicazione: (2025)

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

Multi-fidelity learning for interatomic potentials: Low-level forces and high-level energies are all you need
di: Messerly, Mitchell, et al.
Pubblicazione: (2025)

Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
di: Matin, Sakib, et al.
Pubblicazione: (2025)

Molecular-dynamics study of diffusional creep in uranium mononitride
di: AbdulHameed, Mohamed, et al.
Pubblicazione: (2025)

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

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

Lightweight equivariant model for efficient machine learning interatomic potentials
di: Yang, Ziduo, et al.
Pubblicazione: (2023)

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

The contribution of nitrogen Frenkel-pair formation to the high-temperature heat capacity of uranium mononitride
di: AbdulHameed, Mohamed, et al.
Pubblicazione: (2026)

Knowing when to trust machine-learned interatomic potentials
di: Mehdi, Shams, et al.
Pubblicazione: (2026)

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

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

Pushing the limits of unconstrained machine-learned interatomic potentials
di: Bigi, Filippo, et al.
Pubblicazione: (2026)

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

Ab initio molecular dynamics of paramagnetic uranium mononitride (UN) using disordered local moments
di: AbdulHameed, Mohamed, et al.
Pubblicazione: (2025)

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

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

The transformative capability of quantum-accurate machine learning interatomic potentials
di: Correa, Alfredo A., et al.
Pubblicazione: (2025)

Self-consistent Coulomb interactions for machine learning interatomic potentials
di: Thomas, Jack, et al.
Pubblicazione: (2024)

Cartesian atomic cluster expansion for machine learning interatomic potentials
di: Cheng, Bingqing
Pubblicazione: (2024)

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

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

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

Benchmarking machine-learned interatomic potentials for molecular infrared spectroscopy
di: Bhatia, Nitik, et al.
Pubblicazione: (2026)

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

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

Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review
di: Shingo Urata, et al.
Pubblicazione: (2024)

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

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

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

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

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

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

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

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

Hessian-informed machine learning interatomic potential towards bridging theory and experiments
di: Yin, Bangchen, et al.
Pubblicazione: (2026)

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

Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
di: Stark, Wojciech G., et al.
Pubblicazione: (2024)

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