Quentin Bizot^1*, Noel Jakse^1*

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

* Corresponding authors emails: quentin.bizot@grenoble-inp.fr, noel.jakse@grenoble-inp.fr

DOI10.24435/materialscloud:3h-sc [version v1]

Publication date: Apr 02, 2025

How to cite this record

Quentin Bizot, Noel Jakse, Crystal nucleation in eutectic Al-Si alloys by machine-learned molecular dynamics, Materials Cloud Archive 2025.50 (2025), https://doi.org/10.24435/materialscloud:3h-sc

Description

Solidification control is crucial in manufacturing technologies, as it determines the microstructureand, consequently, the performance of the final product. Investigating the mechanisms occurring during the early stages of nucleation remains experimentally challenging as it initiates on nanometer length and sub-picoseconds time scales. Large scale molecular dynamics simulations using machinelearning interatomic potential with quantum accuracy appears the dedicated approach to complex,atomic level, multidimensional mechanisms with local symmetry breaking. A potential trained ona high-dimensional neural network on density functional theory-based ab initio molecular dynamics(AIMD) trajectories for liquid and undercooled states for Al-Si binary alloys enables us to study the nucleation mechanisms occurring at the early stages from the liquid phase near the eutectic composition. Our results indicate that nucleation starts with Al at hypoeutectic compositions and with Si at hypereutectic compositions. Whereas Al nuclei grow in a globular shape, Si ones grow with polygonal faceting, whose underlying mechanisms are further discussed.

Materials Cloud sections using this data

Files

File name	Size	Description
dataset.AlSi.zip MD5md5:4c2aa17fe6b4aceb8e15e473d5813f7d	345.6 MiB	Training (train.data) and test (test.data) datasets
nnp-data.AlSi.zip MD5md5:f6bab79c3925c5f0b15d8f15c16523c5	112.8 KiB	input.nn, scaling.data, and weight files for the HDNNP potential of Al-Si

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 interatomic potential Aluminum Silicon HDNNP