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