Revealing interstitial energetics in Ti-23Nb-0.7Ta-2Zr gum metal base alloy via universal machine learning interatomic potentials
Fuente:
arXiv
Saved in:
| Main Authors: | Lebeda, Miroslav, Drahokoupil, Jan, Mazáčová, Veronika, Vlčák, Petr |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Rust-accelerated powder X-ray diffraction simulation for high-throughput and machine-learning-driven materials science
by: Lebeda, Miroslav, et al.
Published: (2026)
by: Lebeda, Miroslav, et al.
Published: (2026)
Interactive Analysis of Static, Dynamic, and Crystalline SDTrimSP Simulations: Application to Nitrogen Ion Implantation into Vanadium
by: Lebeda, Miroslav, et al.
Published: (2026)
by: Lebeda, Miroslav, et al.
Published: (2026)
SimplySQS: An Automated and Reproducible Workflow for Special Quasirandom Structure Generation with ATAT
by: Lebeda, Miroslav, et al.
Published: (2025)
by: Lebeda, Miroslav, et al.
Published: (2025)
Lattice Parameters and Bulk Modulus of SrTi$_{1-\mathit{x}}$Mn$_{\mathit{x}}$O$_{3}$ Perovskites: A Comparison of Exchange-Correlation Functionals with Experimental Validation
by: Lebeda, Miroslav, et al.
Published: (2025)
by: Lebeda, Miroslav, et al.
Published: (2025)
Deformation mechanisms and compressive response of NbTaTiZr alloy via machine learning potentials
by: Liu, Hongyang, et al.
Published: (2026)
by: Liu, Hongyang, et al.
Published: (2026)
Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability
by: Mukhamedov, Boburjon, et al.
Published: (2024)
by: Mukhamedov, Boburjon, et al.
Published: (2024)
Fine-tuning of universal machine-learning interatomic potentials for 2D high-entropy alloys
by: Zhou, Chun, et al.
Published: (2026)
by: Zhou, Chun, et al.
Published: (2026)
Superconductivity in Al-based high-entropy alloys TiHfNbTaAl and TaNbHfZrAl
by: Huang, Junjin, et al.
Published: (2026)
by: Huang, Junjin, et al.
Published: (2026)
Nine-element machine-learned interatomic potentials for multiphase refractory alloys
by: Byggmästar, Jesper, et al.
Published: (2026)
by: Byggmästar, Jesper, et al.
Published: (2026)
Effect of cold rolling strain on the microstructural evolution in equimolar MoNbTaTiZr refractory complex concentrated alloy: Comprehensive characterization
by: Skolakova, Andrea, et al.
Published: (2025)
by: Skolakova, Andrea, et al.
Published: (2025)
Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)
by: Chorna, Sofiia, et al.
Published: (2025)
Cross-functional transferability in universal machine learning interatomic potentials
by: Huang, Xu, et al.
Published: (2025)
by: Huang, Xu, et al.
Published: (2025)
Systematic assessment of various universal machine‐learning interatomic potentials
by: Haochen Yu, et al.
Published: (2024)
by: Haochen Yu, et al.
Published: (2024)
Systematic assessment of various universal machine-learning interatomic potentials
by: Yu, Haochen, et al.
Published: (2024)
by: Yu, Haochen, et al.
Published: (2024)
Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
by: Shuang, Fei, et al.
Published: (2025)
by: Shuang, Fei, et al.
Published: (2025)
Supercondutivity of Nb-Ta-Ti-Zr-Hf high entropy alloy polycrystalline and amorphous thin films
by: Hruska, P., et al.
Published: (2025)
by: Hruska, P., et al.
Published: (2025)
Screening of material defects using universal machine-learning interatomic potentials
by: Berger, Ethan, et al.
Published: (2025)
by: Berger, Ethan, et al.
Published: (2025)
Revealing the impact of chemical short-range order on radiation damage in MoNbTaVW high-entropy alloys using a machine-learning potential
by: Liu, Jiahui, et al.
Published: (2025)
by: Liu, Jiahui, et al.
Published: (2025)
Phase Equilibria of the Al-Ti-Nb-Zr-Ta System
by: Kozlík, Jiří, et al.
Published: (2026)
by: Kozlík, Jiří, et al.
Published: (2026)
Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
by: Deng, Bowen, et al.
Published: (2024)
by: Deng, Bowen, et al.
Published: (2024)
The influence of nitrogen ion implantation on the microstructure and chemical composition of a thin layer on the biodegradable Zn-0.8Mg-0.2Sr substrate
by: Pinc, Jan, et al.
Published: (2024)
by: Pinc, Jan, et al.
Published: (2024)
Flexural strength of (Hf,Nb,Ta,Ti,Zr)B 2 –(Hf,Nb,Ta,Ti,Zr)C high‐entropy dual‐phase ceramics
by: Rubia Hassan, et al.
Published: (2025)
by: Rubia Hassan, et al.
Published: (2025)
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
by: Stark, Wojciech G., et al.
Published: (2024)
by: Stark, Wojciech G., et al.
Published: (2024)
Effect of Ti addition on the structural, thermodynamic, and elastic properties of Ti$_{x}$(HfNbTaZr)$_{(1-x)/4}$ alloys
by: Bhatti, Asif I., et al.
Published: (2022)
by: Bhatti, Asif I., et al.
Published: (2022)
Utilizing a machine-learned potential to explore enhanced radiation tolerance in the MoNbTaVW high-entropy alloy
by: Liu, Jiahui, et al.
Published: (2024)
by: Liu, Jiahui, et al.
Published: (2024)
A collinear-spin machine learned interatomic potential for Fe$_{7}$Cr$_{2}$Ni alloy
by: Shenoy, Lakshmi, et al.
Published: (2023)
by: Shenoy, Lakshmi, et al.
Published: (2023)
Effect of C additives with 0.5% in weight on structural, optical and superconducting properties of Ta-Nb-Hf-Zr-Ti high entropy alloy films
by: Le, Tien, et al.
Published: (2025)
by: Le, Tien, et al.
Published: (2025)
MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
by: Kuner, Matthew C., et al.
Published: (2025)
by: Kuner, Matthew C., et al.
Published: (2025)
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
by: Kim, Dongjin, et al.
Published: (2025)
by: Kim, Dongjin, et al.
Published: (2025)
Short-range order and its impacts on the BCC NbMoTaW multi-principal element alloy by the machine-learning potential
by: Santos-Florez, Pedro A., et al.
Published: (2022)
by: Santos-Florez, Pedro A., et al.
Published: (2022)
Thermal boundary conductance of metal diamond interfaces predicted by machine learning interatomic potentials
by: Adnan, Khalid Zobaid, et al.
Published: (2024)
by: Adnan, Khalid Zobaid, et al.
Published: (2024)
Sintering behavior of ZrC, NbC, TaC, and (Zr0.33, Nb0.33, Ta0.33)C and the effects of powder impurities
by: Jonas R. Kessing, et al.
Published: (2025)
by: Jonas R. Kessing, et al.
Published: (2025)
Knowing when to trust machine-learned interatomic potentials
by: Mehdi, Shams, et al.
Published: (2026)
by: Mehdi, Shams, et al.
Published: (2026)
Platonic representation of foundation machine learning interatomic potentials
by: Li, Zhenzhu, et al.
Published: (2025)
by: Li, Zhenzhu, et al.
Published: (2025)
Cartesian atomic moment machine learning interatomic potentials
by: Wen, Mingjian, et al.
Published: (2024)
by: Wen, Mingjian, et al.
Published: (2024)
Pushing the limits of unconstrained machine-learned interatomic potentials
by: Bigi, Filippo, et al.
Published: (2026)
by: Bigi, Filippo, et al.
Published: (2026)
Benchmarking phonon anharmonicity in machine learning interatomic potentials
by: Bandi, Sasaank, et al.
Published: (2024)
by: Bandi, Sasaank, et al.
Published: (2024)
An efficient forgetting-aware fine-tuning framework for pretrained universal machine-learning interatomic potentials
by: Kim, Jisu, et al.
Published: (2025)
by: Kim, Jisu, et al.
Published: (2025)
Superconductivity in new family of Rhenium-based binary alloys: Re$_{7}$X$_{3}$ (X = Nb, Ta, Ti, Zr, Hf)
by: Kushwaha, R. K., et al.
Published: (2024)
by: Kushwaha, R. K., et al.
Published: (2024)
MAD-SURF: a machine learning interatomic potential for molecular adsorption on coinage metal surfaces
by: Lastre, Manuel González, et al.
Published: (2026)
by: Lastre, Manuel González, et al.
Published: (2026)
Similar Items
-
Rust-accelerated powder X-ray diffraction simulation for high-throughput and machine-learning-driven materials science
by: Lebeda, Miroslav, et al.
Published: (2026) -
Interactive Analysis of Static, Dynamic, and Crystalline SDTrimSP Simulations: Application to Nitrogen Ion Implantation into Vanadium
by: Lebeda, Miroslav, et al.
Published: (2026) -
SimplySQS: An Automated and Reproducible Workflow for Special Quasirandom Structure Generation with ATAT
by: Lebeda, Miroslav, et al.
Published: (2025) -
Lattice Parameters and Bulk Modulus of SrTi$_{1-\mathit{x}}$Mn$_{\mathit{x}}$O$_{3}$ Perovskites: A Comparison of Exchange-Correlation Functionals with Experimental Validation
by: Lebeda, Miroslav, et al.
Published: (2025) -
Deformation mechanisms and compressive response of NbTaTiZr alloy via machine learning potentials
by: Liu, Hongyang, et al.
Published: (2026)