Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability
Fuente:
arXiv
Saved in:
| Main Authors: | Mukhamedov, Boburjon, Tasnadi, Ferenc, Abrikosov, Igor A. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Nine-element machine-learned interatomic potentials for multiphase refractory alloys
by: Byggmästar, Jesper, et al.
Published: (2026)
by: Byggmästar, Jesper, et al.
Published: (2026)
Machine-learning interatomic potential for AlN for epitaxial simulation
by: Taormina, Nicholas, et al.
Published: (2025)
by: Taormina, Nicholas, et al.
Published: (2025)
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)
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)
Machine-learning potentials for nanoscale simulations of deformation and fracture: example of TiB$_2$ ceramic
by: Lin, Shuyao, et al.
Published: (2023)
by: Lin, Shuyao, et al.
Published: (2023)
Machine learning interatomic potential can infer electrical response
by: Zhong, Peichen, et al.
Published: (2025)
by: Zhong, Peichen, et al.
Published: (2025)
Revealing interstitial energetics in Ti-23Nb-0.7Ta-2Zr gum metal base alloy via universal machine learning interatomic potentials
by: Lebeda, Miroslav, et al.
Published: (2025)
by: Lebeda, Miroslav, et al.
Published: (2025)
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
by: Zaverkin, Viktor, et al.
Published: (2023)
by: Zaverkin, Viktor, et al.
Published: (2023)
A general-purpose neural network potential for Ti-Al-Nb alloys towards large-scale molecular dynamics with ab initio accuracy
by: Zhao, Zhiqiang, et al.
Published: (2024)
by: Zhao, Zhiqiang, et al.
Published: (2024)
Screening of material defects using universal machine-learning interatomic potentials
by: Berger, Ethan, et al.
Published: (2025)
by: Berger, Ethan, et al.
Published: (2025)
Accelerating point defect photo-emission calculations with machine learning interatomic potentials
by: Sharma, Kartikeya, et al.
Published: (2025)
by: Sharma, Kartikeya, et al.
Published: (2025)
Exact average many-body interatomic interaction model for random alloys
by: Hodapp, Max
Published: (2024)
by: Hodapp, Max
Published: (2024)
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)
Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning
by: Klimanova, Olga, et al.
Published: (2024)
by: Klimanova, Olga, et al.
Published: (2024)
Accurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystals
by: Della Pia, Flaviano, et al.
Published: (2025)
by: Della Pia, Flaviano, et al.
Published: (2025)
Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al and Ni
by: Fellman, Aslak, et al.
Published: (2024)
by: Fellman, Aslak, et al.
Published: (2024)
Combining graph deep learning and London dispersion interatomic potentials: A case study on pnictogen chalcohalides
by: Kılıç, Çetin, et al.
Published: (2024)
by: Kılıç, Çetin, et al.
Published: (2024)
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)
Lattice thermal conductivity and elastic modulus of XN4 (X=Be, Mg and Pt) 2D materials using machine learning interatomic potentials
by: Ghorbani, K., et al.
Published: (2022)
by: Ghorbani, K., et al.
Published: (2022)
Prediction of novel ordered phases in U-X (X= Zr, Sc, Ti, V, Cr, Y, Nb, Mo, Hf, Ta, W) binary alloys under high pressure
by: Pan, Xiao L., et al.
Published: (2024)
by: Pan, Xiao L., 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)
General-purpose machine-learned potential for 16 elemental metals and their alloys
by: Song, Keke, et al.
Published: (2023)
by: Song, Keke, et al.
Published: (2023)
FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential
by: Kang, Hanwen, et al.
Published: (2025)
by: Kang, Hanwen, et al.
Published: (2025)
Long-range electrostatics for machine learning interatomic potentials is easier than we thought
by: Kim, Dongjin, et al.
Published: (2025)
by: Kim, Dongjin, et al.
Published: (2025)
Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
by: Gumber, Shriya, et al.
Published: (2025)
by: Gumber, Shriya, et al.
Published: (2025)
Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost
by: Baghishov, Ilgar, et al.
Published: (2025)
by: Baghishov, Ilgar, et al.
Published: (2025)
Competition between phase ordering and phase segregation in the Ti$_x$NbMoTaW and Ti$_x$VNbMoTaW refractory high-entropy alloys
by: Woodgate, Christopher D., et al.
Published: (2024)
by: Woodgate, Christopher D., et al.
Published: (2024)
Machine learning interatomic potentials for solid-state precipitation
by: Piersante, Lorenzo, et al.
Published: (2026)
by: Piersante, Lorenzo, et al.
Published: (2026)
Suitability of available interatomic potentials for Sn to model its 2D allotropes
by: Maździarz, Marcin
Published: (2024)
by: Maździarz, Marcin
Published: (2024)
Composable and adaptive design of machine learning interatomic potentials guided by Fisher-information analysis
by: Wang, Weishi, et al.
Published: (2025)
by: Wang, Weishi, 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)
Revisiting thermal transport in CuCl: First-principles calculations and machine learning force fields
by: Kundu, Ashis, et al.
Published: (2025)
by: Kundu, Ashis, et al.
Published: (2025)
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)
Machine learning interatomic potential for predicting the thermal properties of uranium nitride
by: Chen, Beihan, et al.
Published: (2025)
by: Chen, Beihan, et al.
Published: (2025)
Beam induced heating in electron microscopy modeled with machine learning interatomic potentials
by: Valencia, Cuauhtemoc Nuñez, et al.
Published: (2023)
by: Valencia, Cuauhtemoc Nuñez, et al.
Published: (2023)
Efficient dataset generation for machine learning perovskite alloys
by: Homm, Henrietta, et al.
Published: (2025)
by: Homm, Henrietta, et al.
Published: (2025)
Unifying the description of hydrocarbons and hydrogenated carbon materials with a chemically reactive machine learning interatomic potential
by: Ibragimova, Rina, et al.
Published: (2024)
by: Ibragimova, Rina, et al.
Published: (2024)
Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
by: Nam, Juno, et al.
Published: (2024)
by: Nam, Juno, et al.
Published: (2024)
How accurate are foundational machine learning interatomic potentials for heterogeneous catalysis?
by: Kempen, Luuk H. E., et al.
Published: (2025)
by: Kempen, Luuk H. E., et al.
Published: (2025)
Similar Items
-
Deformation mechanisms and compressive response of NbTaTiZr alloy via machine learning potentials
by: Liu, Hongyang, et al.
Published: (2026) -
Nine-element machine-learned interatomic potentials for multiphase refractory alloys
by: Byggmästar, Jesper, et al.
Published: (2026) -
Machine-learning interatomic potential for AlN for epitaxial simulation
by: Taormina, Nicholas, et al.
Published: (2025) -
Fine-tuning of universal machine-learning interatomic potentials for 2D high-entropy alloys
by: Zhou, Chun, et al.
Published: (2026) -
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)