Johannes Sandberg^1,2,3*, Thomas Voigtmann^2,3*, Emilie Devijver^4*, Noel Jakse^1*

1 Université Grenoble Alpes, CNRS, Grenoble INP, SIMaP, F-38000 Grenoble, France

2 Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), 51170 Köln, Germany

3 Department of Physics, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany

4 Université Grenoble Alpes, CNRS, Grenoble INP, LIG, F-38000 Grenoble, France

* Corresponding authors emails: johannes.sandberg@grenoble-inp.fr, Thomas.Voigtmann@dlr.de, emilie.devijver@univ-grenoble-alpes.fr, noel.jakse@grenoble-inp.fr

DOI10.24435/materialscloud:dq-ax [version v1]

Publication date: Oct 11, 2024

How to cite this record

Johannes Sandberg, Thomas Voigtmann, Emilie Devijver, Noel Jakse, Homogeneous nucleation of undercooled Al-Ni melts via a machine-learned interaction potential, Materials Cloud Archive 2024.159 (2024), https://doi.org/10.24435/materialscloud:dq-ax

Description

Homogeneous nucleation processes are important for understanding solidification and the resulting microstructure of materials. Simulating this process requires accurately describing the interactions between atoms, hich is further complicated by chemical order through cross-species interactions. The large scales needed to observe rare nucleation events are far beyond the capabilities of ab initio simulations. Machine-learning is used for overcoming these limitations in terms of both accuracy and speed, by building a high-dimensional neural network potential for binary Al-Ni alloys, which serve as a model system relevant to many industrial applications. The potential is validated against experimental diffusion, viscosity, and scattering data, and is applied to large-scale molecular dynamics simulations of homogeneous nucleation at equiatomic composition, as well as for pure Ni. Pure Ni nucleates in a single-step into an fcc crystal phase, in contrast to previous results obtained with a classical empirical potential. This highlights the sensitivity of nucleation pathways to the underlying atomic interactions. Our findings suggest that the nucleation pathway for AlNi proceeds in a single step toward a B2 structure, which is discussed in relation to the pure elements counterparts.

Materials Cloud sections using this data

Files

File name	Size	Description
nnp-data.AlNi.tgz MD5md5:396254658109de25364cac74cae63ecd	114.5 KiB	input, scaling, and weight files, for the potential
dataset-AlNi.tgz MD5md5:4a80a5116b55deabbcee67c9e1b250d6	352.2 MiB	Training and Test Datasets

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

machine learning aluminium nickel interaction potential