Growth driven phase transitions in Zinc Oxide nanoparticles through machine-learning assisted simulations

TL;DR

This paper uses machine-learning assisted simulations to study phase transitions in zinc oxide nanoparticles during synthesis, revealing how deposition processes induce structural changes.

Contribution

It introduces a novel atom-by-atom deposition modeling approach combined with machine learning to understand nanoparticle phase transitions.

Findings

Deposition induces a phase transition from BCT to wurtzite in ZnO nanoparticles.

Ion redistribution during deposition stabilizes the new phase.

Insights aid in designing materials with specific structural features.

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.