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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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Molinier, Rémi
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Publications (5/5 displayed)
- 2022Unsupervised topological learning approach of crystal nucleationcitations
- 2022Unsupervised topological learning approach of crystal nucleationcitations
- 2022Crystal Nucleation in Al-Ni Alloys: an Unsupervised Chemical and Topological Learning Approach
- 2021Unsupervised topological learning approach of crystal nucleation in pure Tantalum
- 2020Glass-forming ability of elemental zirconiumcitations
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document
Unsupervised topological learning approach of crystal nucleation in pure Tantalum
Abstract
We propose an unsupervised machine learning method for the autonomous structural analysis of materials based on original topological descriptors of local atomic structures computed from persistent homology [1, 2]. From this protocol, a model is learned in course of nucleation in order to identify, without a priori on a system, clusters of atomic structures which arise in the process. This method has been applied to investigate the homogeneous nucleation of elemental Tantalum (Ta), on several molecular dynamics configurations of 10 million atoms at the nose of the time-temperature-transformation curve. A general behavior for all the nuclei (even the precritical ones) shows a concurrent emergence of the translational and orientational orderings in this pure metal.