:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Pomes, S., Suzuki, T., Enokizono, T., Adachi, N., Wakeda, M., Ohmura, T.
Format:	Preprint
Published:	2025
Subjects:	Materials Science
Online Access:	https://arxiv.org/abs/2508.03328
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A new nanometre resolution method for probing densification ratio at nanoindentation sites in glass: Unravelling discrepancies in the literature
by: Guin, J. -P., et al.
Published: (2016)

Multiscale nanoindentation modeling of concentrated solid solutions: A continuum plasticity model
by: Frydrych, K., et al.
Published: (2022)

Self-ion irradiation effects on nanoindentation-induced plasticity of crystalline iron: A joint experimental and computational study
by: Mulewska, K., et al.
Published: (2025)

Large-scale atomistic study of plasticity in amorphous gallium oxide with a machine-learning potential
by: Zhang, Jiahui, et al.
Published: (2024)

Geometric indicators of local plasticity in glasses measured by scanning small-beam diffraction
by: Liu, Amelia C. Y., et al.
Published: (2024)

Mechanical softening and enhanced elasticity of lunar olivine probed via nanoindentation and high-pressure X-ray diffraction measurements
by: Grèbol-Tomàs, P., et al.
Published: (2024)

Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability
by: Allec, Sarah I., et al.
Published: (2024)

Coupled electromigration-nanoindentation study on dislocation nucleation in SrTiO3
by: Okafor, Chukwudalu, et al.
Published: (2025)

Defect dependent dynamic nanoindentation hardness of copper up to 25 000 s-1
by: Holz, Hendrik, et al.
Published: (2026)

From cluster to nanocrystal: the continuous evolution and critical size of copper clusters revealed by machine learning
by: Liu, Hongsheng, et al.
Published: (2025)

How precisely are solute clusters in RPV steels characterized by atom probe experiments?
by: Castin, N., et al.
Published: (2024)

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

Thermal transport of glasses via machine learning driven simulations
by: Pegolo, Paolo, et al.
Published: (2024)

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

Constructing multicomponent cluster expansions with machine-learning and chemical embedding
by: Müller, Yann L., et al.
Published: (2024)

Growth and prediction of plastic strain in metallic glasses
by: Mäkinen, Tero, et al.
Published: (2025)

Combining Brillouin spectroscopy and machine learned interatomic potentials to probe mechanical properties of metal organic frameworks
by: Lindner, Florian P., et al.
Published: (2024)

High-temperature behavior of amorphous alumina coatings: Insights from in-situ nanoindentation and X-ray diffraction studies
by: Zaborowska, A., et al.
Published: (2026)

Microstructure sensitive recurrent neural network surrogate model of crystal plasticity
by: Atkinson, Michael D., et al.
Published: (2025)

Atomic-scale phase-field modeling with universal machine learning potentials
by: Masuda, Kairi, et al.
Published: (2025)

Nanoindentation induced plasticity in equiatomic MoTaW alloys by experimentally guided machine learning molecular dynamics simulations
by: Dominguez-Gutierrez, F. J., et al.
Published: (2026)

Efficient small-cell sampling for machine-learning potentials of multi-principal element alloys
by: Liu, Yan, et al.
Published: (2025)

On identifying dynamic length scales in crystal plasticity
by: Berta, Dénes, et al.
Published: (2024)

Topological representation of layered hybrid lead halides for machine-learning using universal clusters
by: Marchenko, Ekaterina I., et al.
Published: (2024)

AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis
by: Allam, Omar, et al.
Published: (2025)

Structural Chart of Copper-Silver Nanoalloys through machine learning
by: Settem, Manoj, et al.
Published: (2026)

Precise large-scale chemical transformations on surfaces: deep learning meets scanning probe microscopy with interpretability
by: Wu, Nian, et al.
Published: (2024)

Elementary processes in dilatational plasticity of glasses
by: Moriel, Avraham, et al.
Published: (2024)

Stochastic evolution elasto-plastic modeling of a metallic glass
by: Xu, Bin, et al.
Published: (2024)

Efficient training of machine learning potentials for metallic glasses: CuZrAl validation
by: Wadowski, Antoni, et al.
Published: (2024)

Elucidating proximity magnetism through polarized neutron reflectometry and machine learning
by: Andrejevic, Nina, et al.
Published: (2021)

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

Understanding solid nitrogen through machine learning simulation
by: Kirsz, Marcin, et al.
Published: (2024)

Cooperative atomic motion during shear deformation in metallic glass
by: Shiihara, Yoshinori, et al.
Published: (2025)

Performance of universal machine learning potentials in global optimization
by: Marcial, Edan T., et al.
Published: (2026)

Synergistic improvement of specific strength and plasticity achieved in Ti-based metallic glass designed based on quasicrystal structure
by: Cai, Zhengqing, et al.
Published: (2026)

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

Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine learning
by: Erhard, Linus C., et al.
Published: (2023)

GEARS H: Accurate machine-learned Hamiltonians for next-generation device-scale modeling
by: Haldar, Anubhab, et al.
Published: (2025)

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