Atomistic understanding of hydrogen bubble-induced embrittlement in tungsten enabled by machine learning molecular dynamics
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Bao, Yu, Song, Keke, Liu, Jiahui, Wang, Yanzhou, Ning, Yifei, Ying, Penghua, Qian, Ping |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics
par: Wu, Xiguang, et autres
Publié: (2024)
par: Wu, Xiguang, et autres
Publié: (2024)
Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials
par: Fan, Zheyong, et autres
Publié: (2023)
par: Fan, Zheyong, et autres
Publié: (2023)
Density dependence of thermal conductivity in nanoporous and amorphous carbon with machine-learned molecular dynamics
par: Wang, Yanzhou, et autres
Publié: (2024)
par: Wang, Yanzhou, et autres
Publié: (2024)
Fast machine learned $α$-Fe-H interatomic potential for hydrogen embrittlement
par: Makkonen, Eetu, et autres
Publié: (2025)
par: Makkonen, Eetu, et autres
Publié: (2025)
Anisotropic and isotropic elasticity and thermal transport in monolayer C$_{24}$ networks from machine-learning molecular dynamics
par: Li, Qing, et autres
Publié: (2025)
par: Li, Qing, et autres
Publié: (2025)
Structural and transport properties of LiTFSI/G3 electrolyte with machine-learned molecular dynamics
par: Cao, Chenyang, et autres
Publié: (2025)
par: Cao, Chenyang, et autres
Publié: (2025)
Fast and accurate Fe-H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms
par: Ito, Kazuma
Publié: (2025)
par: Ito, Kazuma
Publié: (2025)
Solute segregation in polycrystalline aluminum from hybrid Monte Carlo and molecular dynamics simulations with a unified neuroevolution potential
par: Song, Keke, et autres
Publié: (2024)
par: Song, Keke, et autres
Publié: (2024)
An ensemble learning strategy for multi‐source hydrogen embrittlement data by introducing missing information
par: Xujie Gong, et autres
Publié: (2024)
par: Xujie Gong, et autres
Publié: (2024)
Dissimilar thermal transport properties in $κ$-Ga$_2$O$_3$ and $β$-Ga$_2$O$_3$ revealed by machine-learning homogeneous nonequilibrium molecular dynamics simulations
par: Wang, Xiaonan, et autres
Publié: (2023)
par: Wang, Xiaonan, et autres
Publié: (2023)
Revealing the impact of chemical short-range order on radiation damage in MoNbTaVW high-entropy alloys using a machine-learning potential
par: Liu, Jiahui, et autres
Publié: (2025)
par: Liu, Jiahui, et autres
Publié: (2025)
Topological passivation makes high strength alloys insensitive to hydrogen embrittlement
par: Cheng, Huijie, et autres
Publié: (2025)
par: Cheng, Huijie, et autres
Publié: (2025)
Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials
par: Dong, Haikuan, et autres
Publié: (2024)
par: Dong, Haikuan, et autres
Publié: (2024)
General-purpose machine-learned potential for 16 elemental metals and their alloys
par: Song, Keke, et autres
Publié: (2023)
par: Song, Keke, et autres
Publié: (2023)
Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations
par: Zhang, Bohan, et autres
Publié: (2025)
par: Zhang, Bohan, et autres
Publié: (2025)
A coupled fully kinetic hydrogen transport and ductile phase-field fracture framework for modeling hydrogen embrittlement
par: Hussein, Abdelrahman, et autres
Publié: (2026)
par: Hussein, Abdelrahman, et autres
Publié: (2026)
GPUMD 4.0: A high‐performance molecular dynamics package for versatile materials simulations with machine‐learned potentials
par: Ke Xu, et autres
Publié: (2025)
par: Ke Xu, et autres
Publié: (2025)
Advances in modeling complex materials: The rise of neuroevolution potentials
par: Ying, Penghua, et autres
Publié: (2025)
par: Ying, Penghua, et autres
Publié: (2025)
Atomistic insights into hydrogen migration in IGZO from machine-learning interatomic potential: linking atomic diffusion to device performance
par: Cho, Hyunsung, et autres
Publié: (2025)
par: Cho, Hyunsung, et autres
Publié: (2025)
Molecular dynamics study of the role of anisotropy in radiation-driven embrittlement
par: Mousavi, Hojjat, et autres
Publié: (2026)
par: Mousavi, Hojjat, et autres
Publié: (2026)
Heat transport in superionic materials via machine-learned molecular dynamics
par: Zhou, Wenjiang, et autres
Publié: (2025)
par: Zhou, Wenjiang, et autres
Publié: (2025)
Atomistic simulations of irradiation damage on the engineering timescale: Examining the dose rate effect in tungsten
par: Boleininger, Max, et autres
Publié: (2025)
par: Boleininger, Max, et autres
Publié: (2025)
Atomistic simulations of athermal irradiation creep and swelling of copper and tungsten in the high dose limit
par: Reali, Luca, et autres
Publié: (2024)
par: Reali, Luca, et autres
Publié: (2024)
Thermal gradient effect on hydrogen transport in tungsten
par: Alturk, Sanad, et autres
Publié: (2025)
par: Alturk, Sanad, et autres
Publié: (2025)
Atomistic modeling of uranium monocarbide with a machine learning interatomic potential
par: Alzate-Vargas, Lorena, et autres
Publié: (2025)
par: Alzate-Vargas, Lorena, et autres
Publié: (2025)
An empirical potential to simulate helium and hydrogen in highly irradiated tungsten
par: Tirumala, Samanyu, et autres
Publié: (2025)
par: Tirumala, Samanyu, et autres
Publié: (2025)
Cluster dynamics modeling of hydrogen saturation retention in tungsten with a universal trapping-site sink strength
par: Zhang, Yuanyuan, et autres
Publié: (2025)
par: Zhang, Yuanyuan, et autres
Publié: (2025)
Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten
par: Zhang, Xi, et autres
Publié: (2023)
par: Zhang, Xi, et autres
Publié: (2023)
NEP-MB-pol: A unified machine-learned framework for fast and accurate prediction of water's thermodynamic and transport properties
par: Xu, Ke, et autres
Publié: (2024)
par: Xu, Ke, et autres
Publié: (2024)
Nanoindentation induced plasticity in equiatomic MoTaW alloys by experimentally guided machine learning molecular dynamics simulations
par: Dominguez-Gutierrez, F. J., et autres
Publié: (2026)
par: Dominguez-Gutierrez, F. J., et autres
Publié: (2026)
Cryogenic hydrogen embrittlement of 316plus (EN 1.4420) stainless steel at 77 K and 20 K
par: Li, W., et autres
Publié: (2026)
par: Li, W., et autres
Publié: (2026)
A first-principles study of Zn induced liquid metal embrittlement at bcc and fcc grain boundaries
par: Saikia, Ujjal, et autres
Publié: (2024)
par: Saikia, Ujjal, et autres
Publié: (2024)
The effect of multi-occupancy traps on the diffusion and retention of multiple hydrogen isotopes in irradiated tungsten and vanadium
par: Kaur, Sanjeet, et autres
Publié: (2025)
par: Kaur, Sanjeet, et autres
Publié: (2025)
Atomistic understanding of hydrogen coverage on RuO2(110) surface under electrochemical conditions from ab initio statistical thermodynamics
par: Zhang, Lei, et autres
Publié: (2024)
par: Zhang, Lei, et autres
Publié: (2024)
Utilizing a machine-learned potential to explore enhanced radiation tolerance in the MoNbTaVW high-entropy alloy
par: Liu, Jiahui, et autres
Publié: (2024)
par: Liu, Jiahui, et autres
Publié: (2024)
Softening of Vibrational Modes and Anharmonicity Induced Thermal Conductivity Reduction in a‐Si:H at High Temperatures
par: Zhuo Chen, et autres
Publié: (2025)
par: Zhuo Chen, et autres
Publié: (2025)
Mode selectivity in electron promoted vibrational relaxation of chemisorbed hydrogen on molybdenum and tungsten surfaces
par: Hertl, Nils, et autres
Publié: (2025)
par: Hertl, Nils, et autres
Publié: (2025)
Melting of thin silicon films: a molecular dynamics study with two machine learning potentials
par: Fomin, Yu. D., et autres
Publié: (2026)
par: Fomin, Yu. D., et autres
Publié: (2026)
Decoding the hidden dynamics of super-Arrhenius hydrogen diffusion in multi-principal element alloys via machine learning
par: Shuang, Fei, et autres
Publié: (2024)
par: Shuang, Fei, et autres
Publié: (2024)
Softening of Vibrational Modes and Anharmonicity Induced Thermal Conductivity Reduction in a-Si:H at High Temperatures
par: Chen, Zhuo, et autres
Publié: (2025)
par: Chen, Zhuo, et autres
Publié: (2025)
Documents similaires
-
Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics
par: Wu, Xiguang, et autres
Publié: (2024) -
Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials
par: Fan, Zheyong, et autres
Publié: (2023) -
Density dependence of thermal conductivity in nanoporous and amorphous carbon with machine-learned molecular dynamics
par: Wang, Yanzhou, et autres
Publié: (2024) -
Fast machine learned $α$-Fe-H interatomic potential for hydrogen embrittlement
par: Makkonen, Eetu, et autres
Publié: (2025) -
Anisotropic and isotropic elasticity and thermal transport in monolayer C$_{24}$ networks from machine-learning molecular dynamics
par: Li, Qing, et autres
Publié: (2025)