About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Unsupervised Topological Learning Approach for Crystal Nucleation in Pure Metals and Alloys |
Author(s) |
Sebastien Becker, Emilie Devijver, Rémi Molinier, Philippe Jarry, Noel Jakse |
On-Site Speaker (Planned) |
Noel Jakse |
Abstract Scope |
Theoretical understanding of crystal nucleation is still a challenging issue as experimental confirmation remains out of reach for bulk materials. Large-scale atomic-level simulations are therefore a promising substitute for such experiments, and molecular dynamics (MD) of million to billion atoms may indeed lead to meaningful results. Machine Learning (ML) tools propose powerful methods to analyse such a large amount of MD-generated big data. An unsupervised ML approach based on topological descriptors using persistent homology concepts is proposed to reveal the structural features of atomic arrangements without a priori knowledge on the studied system. This approach is applied to monatomic metals and extended to aluminium-based alloys. Both translational and orientational orderings are thus evidenced together with nucleation pathways, whose revealed features are beyond the hypotheses of the Classical Nucleation Theory. This promising methodology more generally opens the route to an autonomous and in-depth investigation of atomic level mechanisms in material science. |
Proceedings Inclusion? |
Undecided |