Expanding the search space of high entropy oxides and predicting synthesizability using machine learning interatomic potentials
Fuente:
arXiv
Saved in:
| Main Authors: | Dicks, Oliver A., Aamlid, Solveig S., Hallas, Alannah M., Rottler, Joerg |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Short-range order and local distortions in entropy stabilized oxides
by: Aamlid, Solveig S., et al.
Published: (2024)
by: Aamlid, Solveig S., et al.
Published: (2024)
Effect of high pressure synthesis conditions on the formation of high entropy oxides
by: Aamlid, Solveig Stubmo, et al.
Published: (2024)
by: Aamlid, Solveig Stubmo, et al.
Published: (2024)
Impact of synthesis method on the structure and function of high entropy oxides
by: González-Rivas, Mario U., et al.
Published: (2024)
by: González-Rivas, Mario U., et al.
Published: (2024)
Understanding the role of entropy in high entropy oxides
by: Aamlid, Solveig S., et al.
Published: (2023)
by: Aamlid, Solveig S., et al.
Published: (2023)
Constructing and evaluating machine-learned interatomic potentials for Li-based disordered rocksalts
by: Choyal, Vijay, et al.
Published: (2023)
by: Choyal, Vijay, et al.
Published: (2023)
Analysis of local structure of mechanical and thermal rearrangements in glasses with the atomic cluster expansion
by: Rottler, Joerg, et al.
Published: (2024)
by: Rottler, Joerg, et al.
Published: (2024)
Unveiling the crystallization kinetics in Ge-rich Ge$_x$Te alloys by large scale simulations with a machine-learned interatomic potential
by: Baratella, Dario, et al.
Published: (2024)
by: Baratella, Dario, et al.
Published: (2024)
On phase separation and crystallization of Ge-rich GeSbTe alloys from atomistic simulations with a machine learning interatomic potential
by: Kheir, Omar Abou El, et al.
Published: (2026)
by: Kheir, Omar Abou El, et al.
Published: (2026)
Comparison of intermediate-range order in GeO$_2$ glass: molecular dynamics using machine-learning interatomic potential vs.\ reverse Monte Carlo fitting to experimental data
by: Matsutani, Kenta, et al.
Published: (2024)
by: Matsutani, Kenta, et al.
Published: (2024)
Localization of vibrational modes in high-entropy oxides
by: Wilson, C. M., et al.
Published: (2023)
by: Wilson, C. M., et al.
Published: (2023)
Efficient training of machine learning potentials for metallic glasses: CuZrAl validation
by: Wadowski, Antoni, et al.
Published: (2024)
by: Wadowski, Antoni, et al.
Published: (2024)
Electronic structure prediction of medium and high entropy alloys across composition space
by: Pathrudkar, Shashank, et al.
Published: (2024)
by: Pathrudkar, Shashank, et al.
Published: (2024)
AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials
by: Bidoggia, Davide, et al.
Published: (2025)
by: Bidoggia, Davide, et al.
Published: (2025)
Thermal transport of glasses via machine learning driven simulations
by: Pegolo, Paolo, et al.
Published: (2024)
by: Pegolo, Paolo, et al.
Published: (2024)
Equivariant graph neural network interatomic potential for Green-Kubo thermal conductivity in phase change materials
by: Lee, Sung-Ho, et al.
Published: (2023)
by: Lee, Sung-Ho, et al.
Published: (2023)
Hydrogen liquid-liquid transition from first principles and machine learning
by: Tenti, Giacomo, et al.
Published: (2025)
by: Tenti, Giacomo, et al.
Published: (2025)
RAFFLE: Active learning accelerated interface structure prediction
by: Taylor, Ned Thaddeus, et al.
Published: (2025)
by: Taylor, Ned Thaddeus, et al.
Published: (2025)
Experimentally validated and empirically compared machine learning approach for predicting yield strength of additively manufactured multi-principal element alloys from Co-Cr-Fe-Mn-Ni system
by: Chandraker, Abhinav, et al.
Published: (2023)
by: Chandraker, Abhinav, et al.
Published: (2023)
Scalable platform enabling reservoir computing with nanoporous oxide memristors for image recognition and time series prediction
by: Donald, Joshua, et al.
Published: (2026)
by: Donald, Joshua, et al.
Published: (2026)
Relationship between the shear modulus and volume relaxation in high-entropy metallic glasses: experiment and physical origin
by: Khmyrov, R. S., et al.
Published: (2024)
by: Khmyrov, R. S., et al.
Published: (2024)
Unraveling the role of disorder in the electronic structure of high entropy alloys
by: Bhatt, Neeraj, et al.
Published: (2025)
by: Bhatt, Neeraj, et al.
Published: (2025)
The impact of physicochemical features of carbon electrodes on the capacitive performance of supercapacitors: A machine learning approach
by: Mishra, Sachit, et al.
Published: (2022)
by: Mishra, Sachit, et al.
Published: (2022)
Structural properties of amorphous Na$_3$OCl electrolyte by first-principles and machine learning molecular dynamics
by: Pham, T. -L., et al.
Published: (2024)
by: Pham, T. -L., et al.
Published: (2024)
Machine learning potential as a guide for eutectic in ultra-refractory multicomponent ceramics
by: Valiulin, V. E., et al.
Published: (2026)
by: Valiulin, V. E., et al.
Published: (2026)
Efficiently charting the space of mixed vacancy-ordered perovskites by machine-learning encoded atomic-site information
by: Zhang, Fan, et al.
Published: (2025)
by: Zhang, Fan, et al.
Published: (2025)
High entropy ceramics for applications in extreme environments
by: Ward, T. Z., et al.
Published: (2024)
by: Ward, T. Z., et al.
Published: (2024)
Self-consistency error correction for accurate machine learning potentials from variational Monte Carlo
by: Tenti, Giacomo, et al.
Published: (2025)
by: Tenti, Giacomo, et al.
Published: (2025)
Disentangling competing interactions in disordered materials using interaction space modelling
by: Schmidt, Ella M., et al.
Published: (2024)
by: Schmidt, Ella M., et al.
Published: (2024)
Revisiting the machine-learning density functional for the one-dimensional Hubbard model with random external potential
by: Salmon, Octavio D. R., et al.
Published: (2026)
by: Salmon, Octavio D. R., et al.
Published: (2026)
Amorphous silicon structures generated using a moment tensor potential and the activation relaxation technique nouveau
by: Zongo, Karim, et al.
Published: (2025)
by: Zongo, Karim, et al.
Published: (2025)
Estimating predictability of depinning dynamics by machine learning
by: Haavisto, Valtteri, et al.
Published: (2023)
by: Haavisto, Valtteri, et al.
Published: (2023)
Transferable potential for molecular dynamics simulations of borosilicate glasses and structural comparison of machine learning optimized parameters
by: Yang, Kai, et al.
Published: (2025)
by: Yang, Kai, et al.
Published: (2025)
A cost-effective strategy of enhancing machine learning potentials by transfer learning from a multicomponent dataset on ænet-PyTorch
by: Aisnadaa, An Niza El, et al.
Published: (2024)
by: Aisnadaa, An Niza El, et al.
Published: (2024)
Growth and prediction of plastic strain in metallic glasses
by: Mäkinen, Tero, et al.
Published: (2025)
by: Mäkinen, Tero, et al.
Published: (2025)
Extreme disorder in crystalline perovskite oxide: a new paradigm in quantum materials research
by: Middey, Srimanta, et al.
Published: (2025)
by: Middey, Srimanta, et al.
Published: (2025)
Unsupervised machine learning for supercooled liquids
by: Qiu, Yunrui, et al.
Published: (2024)
by: Qiu, Yunrui, et al.
Published: (2024)
Phonon predictions with E(3)-equivariant graph neural networks
by: Fang, Shiang, et al.
Published: (2024)
by: Fang, Shiang, et al.
Published: (2024)
Non-isothermal stress relaxation in conventional and high-entropy metallic glasses and its relationship to themixing and excess entropy
by: Afonin, G. V., et al.
Published: (2025)
by: Afonin, G. V., et al.
Published: (2025)
Coercivity influence of nanostructure in SmCo-1:7 magnets: Machine learning of high-throughput micromagnetic data
by: Yang, Yangyiwei, et al.
Published: (2024)
by: Yang, Yangyiwei, et al.
Published: (2024)
Machine learning the local electronic density of states
by: Aryanpour, A., et al.
Published: (2025)
by: Aryanpour, A., et al.
Published: (2025)
Similar Items
-
Short-range order and local distortions in entropy stabilized oxides
by: Aamlid, Solveig S., et al.
Published: (2024) -
Effect of high pressure synthesis conditions on the formation of high entropy oxides
by: Aamlid, Solveig Stubmo, et al.
Published: (2024) -
Impact of synthesis method on the structure and function of high entropy oxides
by: González-Rivas, Mario U., et al.
Published: (2024) -
Understanding the role of entropy in high entropy oxides
by: Aamlid, Solveig S., et al.
Published: (2023) -
Constructing and evaluating machine-learned interatomic potentials for Li-based disordered rocksalts
by: Choyal, Vijay, et al.
Published: (2023)