Year: 2024
Type: article
Abstract: Despite numerous efforts from numerical approaches to complement experimental measurements, several fundamental challenges have still hindered one’s ability to truly provide an atomistic picture of the nucleation process in nanocrystals. Among them, our study resolves three obstacles: (1) Machine-learning force fields including long-range interactions able to capture the finesse of the underlying atomic interactions, (2) Data-driven characterization of the local ordering in a complex structural landscape associated with several crystal polymorphs and (3) Comparing results from a large range of temperatures using both brute-force and rare-event sampling. Altogether, our simulation strategy has allowed us to study zinc oxide crystallization from nano-droplet melt. Remarkably, our results show that different nucleation pathways compete depending on the investigated degree of supercooling.
Source: npj Computational Materials
Institutions: Centre National de la Recherche Scientifique, École Nationale Supérieure de Chimie de Lille, Université de Lille, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Centre d’Élaboration de Matériaux et d’Études Structurales, Unité Matériaux et Transformations, Sorbonne Université, Institut des NanoSciences de Paris
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,290
OpenAlex ID: W4402034829