:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Annevelink, Emil, Shankar, Varun
Formato:	Preprint
Publicado:	2026
Materias:	Materials Science
Acceso en línea:	https://arxiv.org/abs/2602.02228
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Data-efficient multi-fidelity training for high-fidelity machine learning interatomic potentials
por: Kim, Jaesun, 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)

An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
por: Kim, Jisu, 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)

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

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

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)

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

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

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

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

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

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

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

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

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

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

A practical guide to machine learning interatomic potentials -- Status and future
por: Jacobs, Ryan, 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)

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)

Controlling moving interfaces in solid state batteries
por: Mosleh, Salem, et al.
Publicado: (2024)

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)

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

Fast and accurate Fe-H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms
por: Ito, Kazuma
Publicado: (2025)

Structure-property relations of silicon oxycarbides studied using a machine learning interatomic potential
por: Leimeroth, Niklas, et al.
Publicado: (2024)

AtomProNet: Data flow to and from machine learning interatomic potentials in materials science
por: Galib, Musanna, et al.
Publicado: (2025)

Studies of Ni-Cr complexation in FLiBe molten salt using machine learning interatomic potentials
por: Attarian, Siamak, et al.
Publicado: (2024)

Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials
por: Zhong, Peichen, et al.
Publicado: (2025)

Radiation damage and phase stability of Al$_x$CrCuFeNi$_y$ alloys using a machine-learned interatomic potential
por: Fellman, Aslak, et al.
Publicado: (2025)

Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead
por: Hellyar, Tom, et al.
Publicado: (2026)

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

Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
por: Deng, Bowen, et al.
Publicado: (2024)

Constructing and evaluating machine-learned interatomic potentials for Li-based disordered rocksalts
por: Choyal, Vijay, et al.
Publicado: (2023)