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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
Interatomic force fields for zirconium based on the embedded atom method and the tabulated Gaussian Approximation Potential
Abstract
The accuracy of interatomic interaction potentials - also known as force fields - is the main factor determining the physical soundness of classical molecular dynamics (MD) simulations. Here, we present a multi-objective framework to generate embedded-atom-method (EAM) force fields using ab initio data. The EAM force fields were tuned via particle swarm optimization to capture the non-linear association between atomic structures and system energies. Using this framework, 95 standard EAM force fields for zirconium were developed and 45 physical features for each developed force field were tracked. Principal component analysis (PCA) was performed to provide insights into the compromises that must be made when generating EAM force fields. Of note, by assigning large fitting weights to generalized stacking fault energy (GSFE) surfaces, there exist EAM force fields with properly positioned minima on prismatic GSFE surfaces and containing no spurious minima in basal GSFE surfaces. However, while standard EAM force fields achieved this without explicitly taking the angular dependence of atomic interactions into account, they led to a severe mismatch between other important physical properties and benchmarks. Hence, we also constructed two machine-learned tabulated Gaussian approximation potentials (tabGAP) with an additional three-body term, which successfully tackled the aforementioned issue and exhibit acceptable prediction accuracy across many physical properties (lattice parameters, elastic properties, dimer potential energies, melting temperatures, phase stability, point defect formation energies, point defect migration energies, and free surface energies) of Zr. Remarkably, its computational efficiency is only 6 times slower than standard EAM force fields.