Evaluating and improving the predictive accuracy of mixing enthalpies and volumes in disordered alloys from universal pre-trained machine learning potentials
Fuente:
arXiv
Saved in:
| Main Authors: | Casillas-Trujillo, Luis, Parackal, Abhijith S., Armiento, Rickard, Alling, Björn |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials
by: Parackal, Abhijith S, et al.
Published: (2026)
by: Parackal, Abhijith S, et al.
Published: (2026)
Predicting the Curie temperature in substitutionally disordered alloys using a first-principles based model
by: Brännvall, Marian Arale, et al.
Published: (2024)
by: Brännvall, Marian Arale, et al.
Published: (2024)
Predicting the Curie temperature of magnetic materials with automated calculations across chemistries and structures
by: Brännvall, Marian Arale, et al.
Published: (2024)
by: Brännvall, Marian Arale, et al.
Published: (2024)
Screening 39 billion protostructures for materials discovery
by: Parackal, Abhijith S, et al.
Published: (2026)
by: Parackal, Abhijith S, et al.
Published: (2026)
Identifying Crystal Structures Beyond Known Prototypes from X-ray Powder Diffraction Spectra
by: Parackal, Abhijith S., et al.
Published: (2023)
by: Parackal, Abhijith S., et al.
Published: (2023)
Na in Diamond: High Spin Defects Revealed by the ADAQ High-Throughput Computational Database
by: Davidsson, Joel, et al.
Published: (2023)
by: Davidsson, Joel, et al.
Published: (2023)
WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
by: Kelvinius, Filip Ekström, et al.
Published: (2025)
by: Kelvinius, Filip Ekström, et al.
Published: (2025)
Accelerated predictions of the sublimation enthalpy of organic materials with machine learning
by: Yifan Liu, et al.
Published: (2025)
by: Yifan Liu, et al.
Published: (2025)
High-Throughput Exploration of NV-like Color Centers Across Host Materials
by: Groppfeldt, Oscar, et al.
Published: (2025)
by: Groppfeldt, Oscar, 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)
Order-disorder duality of high entropy alloys extends non-linear optics
by: Milichko, Valentin A., et al.
Published: (2025)
by: Milichko, Valentin A., et al.
Published: (2025)
Upward band gap bowing and negative mixing enthalpy in multi-component cubic halide perovskite alloys
by: Zhang, Xiuwen, et al.
Published: (2026)
by: Zhang, Xiuwen, et al.
Published: (2026)
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)
The Chlorine Vacancy in 4H-SiC: An NV-like Defect With Telecom Emission
by: Bulancea-Lindvall, Oscar, et al.
Published: (2023)
by: Bulancea-Lindvall, Oscar, et al.
Published: (2023)
Capturing short-range order in high-entropy alloys with machine learning potentials
by: Cao, Yifan, et al.
Published: (2024)
by: Cao, Yifan, et al.
Published: (2024)
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)
Nine-element machine-learned interatomic potentials for multiphase refractory alloys
by: Byggmästar, Jesper, et al.
Published: (2026)
by: Byggmästar, Jesper, et al.
Published: (2026)
Performance of universal machine learning potentials in global optimization
by: Marcial, Edan T., et al.
Published: (2026)
by: Marcial, Edan T., et al.
Published: (2026)
ADAQ-SYM: Automated Symmetry Analysis of Defect Orbitals
by: Stenlund, William, et al.
Published: (2023)
by: Stenlund, William, et al.
Published: (2023)
Composition based machine learning to predict phases & strength of refractory high entropy alloys
by: Iyengar, M. Sreenidhi, et al.
Published: (2025)
by: Iyengar, M. Sreenidhi, et al.
Published: (2025)
Data-driven study of the enthalpy of mixing in the liquid phase
by: Deffrennes, Guillaume, et al.
Published: (2024)
by: Deffrennes, Guillaume, et al.
Published: (2024)
Efficient small-cell sampling for machine-learning potentials of multi-principal element alloys
by: Liu, Yan, et al.
Published: (2025)
by: Liu, Yan, et al.
Published: (2025)
Amending CALPHAD databases using a neural network for predicting mixing enthalpy of liquids
by: Vincely, Clement, et al.
Published: (2025)
by: Vincely, Clement, et al.
Published: (2025)
Systematic assessment of various universal machine-learning interatomic potentials
by: Yu, Haochen, et al.
Published: (2024)
by: Yu, Haochen, et al.
Published: (2024)
High-Throughput Quantification of Altermagnetic Band Splitting
by: Sufyan, Ali, et al.
Published: (2025)
by: Sufyan, Ali, et al.
Published: (2025)
Effects of W alloying on the electronic structure, phase stability and thermoelectric power factor in epitaxial CrN thin films
by: Singh, Niraj Kumar, et al.
Published: (2023)
by: Singh, Niraj Kumar, et al.
Published: (2023)
Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials
by: Matin, Sakib, et al.
Published: (2025)
by: Matin, Sakib, et al.
Published: (2025)
Atomic-scale phase-field modeling with universal machine learning potentials
by: Masuda, Kairi, et al.
Published: (2025)
by: Masuda, Kairi, 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)
Theoretical characterization of NV-like defects in 4H-SiC using ADAQ with the SCAN and r2SCAN meta-GGA functionals
by: Abbas, Ghulam, et al.
Published: (2025)
by: Abbas, Ghulam, et al.
Published: (2025)
Improving robustness and training efficiency of machine-learned potentials by incorporating short-range empirical potentials
by: Yan, Zihan, et al.
Published: (2025)
by: Yan, Zihan, 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)
Upscaling DFT-trained machine-learning interatomic potential toward Quantum Monte Carlo accuracy: Sulfur-vacancy migration in monolayer MoS$_2$ as a testbed
by: Hložný, Adam, et al.
Published: (2026)
by: Hložný, Adam, et al.
Published: (2026)
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)
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
by: Noordhoek, Kyle, et al.
Published: (2023)
by: Noordhoek, Kyle, et al.
Published: (2023)
Machine learning to explore high-entropy alloys with desired enthalpy for room-temperature hydrogen storage: Prediction of density functional theory and experimental data
by: Dangwal, Shivam, et al.
Published: (2024)
by: Dangwal, Shivam, et al.
Published: (2024)
On-the-fly training of polynomial machine learning potentials in computing lattice thermal conductivity
by: Togo, Atsushi, et al.
Published: (2024)
by: Togo, Atsushi, et al.
Published: (2024)
Comparing the latent features of universal machine-learning interatomic potentials
by: Chorna, Sofiia, et al.
Published: (2025)
by: Chorna, Sofiia, 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)
Similar Items
-
Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials
by: Parackal, Abhijith S, et al.
Published: (2026) -
Predicting the Curie temperature in substitutionally disordered alloys using a first-principles based model
by: Brännvall, Marian Arale, et al.
Published: (2024) -
Predicting the Curie temperature of magnetic materials with automated calculations across chemistries and structures
by: Brännvall, Marian Arale, et al.
Published: (2024) -
Screening 39 billion protostructures for materials discovery
by: Parackal, Abhijith S, et al.
Published: (2026) -
Identifying Crystal Structures Beyond Known Prototypes from X-ray Powder Diffraction Spectra
by: Parackal, Abhijith S., et al.
Published: (2023)