| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Byggmästar, Jesper
University of Helsinki
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (16/16 displayed)
- 2024Understanding the RBS/c spectra of irradiated tungsten : A computational study
- 2024Understanding the RBS/c spectra of irradiated tungsten
- 2024Solubility of Hydrogen in a WMoTaNbV High-Entropy Alloycitations
- 2024Nanoindentation of tungsten : From interatomic potentials to dislocation plasticity mechanismscitations
- 2024Interatomic force fields for zirconium based on the embedded atom method and the tabulated Gaussian Approximation Potential
- 2023Nanoindentation of tungstencitations
- 2023Simple machine-learned interatomic potentials for complex alloyscitations
- 2023Self-ion irradiation of high purity ironcitations
- 2023Comprehensive structural changes in nanoscale-deformed silicon modelled with an integrated atomic potentialcitations
- 2022Simple machine-learned interatomic potentials for complex alloyscitations
- 2021Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentialscitations
- 2021Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentials : Defects and segregationcitations
- 2021Machine-learning interatomic potential for W-Mo alloyscitations
- 2020Insights into the primary radiation damage of silicon by a machine learning interatomic potentialcitations
- 2019Cascade overlap with vacancy-type defects in Fecitations
- 2018Defect structures and statistics in overlapping cascade damage in fusion-relevant bcc metalscitations
Places of action
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article
Simple machine-learned interatomic potentials for complex alloys
Abstract
Developing data-driven machine-learning interatomic potential for materials containing many elements becomes increasingly challenging due to the vast configuration space that must be sampled by the training data. We study the learning rates and achievable accuracy of machine-learning interatomic potentials for many-element alloys with different combinations of descriptors for the local atomic environments. We show that for a five-element alloy system, potentials using simple low-dimensional descriptors can reach meV/atom-accuracy with modestly sized training datasets, significantly outperforming the high-dimensional SOAP descriptor in data efficiency, accuracy, and speed. In particular, we develop a computationally fast machine-learned and tabulated Gaussian approximation potential (tabGAP) for Mo-Nb-Ta-V-W alloys with a combination of two-body, three-body, and a new simple scalar many-body density descriptor based on the embedded atom method.