Growth driven phase transitions in zinc oxide nanoparticles through machine-learning assisted simulations

Read full text
Authors: Quentin Gromoff, Magali Benoit, Jacek Goniakowski, Carlos R. Salazar, Julien Lam
Cited by: 0

Abstract

This study investigates the formation of zinc oxide (ZnO) nanoparticles, a material of significant technological interest with complex structural properties, through atom-by-atom deposition modeling-a process common in bottom-up synthesis. Our findings demonstrate that, although the body-centered tetragonal (BCT) structure is thermodynamically stable at equilibrium for small particle sizes, the deposition process induces a crystal-to-crystal phase transition into the more stable wurtzite (WRZ) phase. This transformation is facilitated by a specific redistribution of the nanoparticle ions, which effectively compensates the emerging polar facets at the moment of transition. These insights offer a deeper understanding of oxide nanoparticle formation, which should ultimately help the design of materials with targeted structural features.

Fields of Study & Subjects

Machine Learning in Materials Science Block Copolymer Self-Assembly nanoparticles nucleation surface interactions

Publication details

Journal / source
Nanoscale
Volume
18
Issue
24
Pages
12903–12912
Publication date
1 January 2026
Language
EN