<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Bizot, Quentin</dc:creator> <dc:creator>Jakse, Noel</dc:creator> <dc:date>2025-04-02</dc:date> <dc: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.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2025.50</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:3h-sc</dc:identifier> <dc:identifier>mcid:2025.50</dc:identifier> <dc:identifier>oai:materialscloud.org:2621</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>Machine learning interatomic potential</dc:subject> <dc:subject>Aluminum Silicon</dc:subject> <dc:subject>HDNNP</dc:subject> <dc:title>Crystal nucleation in eutectic Al-Si alloys by machine-learned molecular dynamics</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>