Machine learning simulation reveals phase transition due to growth of zinc oxide nanoparticles

Zinc oxide nanoparticles pose an interesting challenge to materials scientists because they exhibit the complex structural behavior that determines their properties, and researchers have now gained unprecedented insight into their formation. Colleagues Quentin Gromoff and Magali Benoit from CEMES, CNRS, and the University of Toulouse, along with Jacek Goniakowski and Carlos R. Salazar. Graduated from CNRS, Sorbonne University, Sorbonne University. Riel et al. demonstrated how these nanoparticles undergo surprising phase transitions during growth. Their work revealed that although one crystalline structure is favored at equilibrium, the process of building these particles atom by atom is actually caused by a specific redistribution of ions within the structure, promoting a shift toward a more stable configuration. This discovery significantly advances our understanding of nanoparticle formation and paves the way for the design of materials with precisely tailored structural properties.

Growth and phase transition of ZnO nanoparticles

In this study, we investigate the formation of zinc oxide (ZnO) nanoparticles, a technologically important material, and investigate the growth-induced phase transitions in these structures. By combining advanced computational simulations and machine learning techniques, researchers can predict and analyze these transitions, providing insights beyond traditional experimental methods. Coarse-grained molecular dynamics simulations, accelerated by the potential of machine learning trained on fundamental calculations, enable the investigation of nanoparticle growth under different conditions, revealing pathways to different structural phases and identifying key parameters influencing morphology. This study demonstrates the ability to accurately predict the structural evolution of ZnO nanoparticles and provides a valuable tool for materials design and optimization.

This finding indicates that the body-centered tetragonal structure is thermodynamically stable at small grain size equilibrium, but the deposition process induces an intercrystalline phase transition to the more stable wurtzite phase. This transformation is facilitated by a specific redistribution of nanoparticle ions, effectively compensating for the polar facets that emerge as the structure evolves, providing a deeper understanding of how nanoparticle structures arise during synthesis.

Growth of ZnO nanoparticles governed by surface polarity

In this study, we study the nucleation and growth mechanisms of zinc oxide (ZnO) nanoparticles, focusing on the effects of surface polarity and long-range electrostatic interactions. The authors utilize fundamental calculations, machine learning possibilities, and molecular dynamics simulations to understand the formation and stability of different ZnO crystal structures. This study highlights the important role of surface polarity in determining the preferred growth direction and final morphology of ZnO nanoparticles, as polar surfaces exhibit a tendency to remodel or stabilize through the formation of facets with reduced polarity. Researchers developed and utilized advanced machine learning potentials that incorporate long-range electrostatic interactions to accurately model interatomic forces and enable large-scale simulations. The simulations revealed the relative stability of different ZnO crystal structures under different conditions, provided insight into the factors governing their formation, investigated competing nucleation pathways leading to different structures, and identified initial steps and energy barriers.

These forces have a significant impact on the stability and morphology of polar surfaces, highlighting the importance of accurately capturing long-range electrostatic interactions in machine learning potentials. This study provides a detailed understanding of the complex interplay between surface polarity, electrostatic interactions, and crystal structure in the formation of ZnO nanoparticles. This is important for controlling the synthesis and properties of these materials for applications such as catalysis, sensing, and optoelectronics.

Potential applications of this research include controlled nanoparticle synthesis, materials design, optimization of ZnO catalysts, development of more sensitive sensors, and improvement of ZnO-based optoelectronic devices.

Growth defines the structure of wurtzite zinc oxide

This study provides new insights into the formation of zinc oxide nanoparticles by modeling their growth in atom-by-atom detail. The researchers demonstrated that although the body-centered tetragonal structure thermodynamically favors small particles, the deposition process induces a transition to the more stable wurtzite phase. This transformation occurs through a specific redistribution of ions within the nanoparticles, effectively compensating the polar plane as the structure evolves, expanding our understanding of how oxide nanoparticles are formed and providing a basis for designing materials with targeted structural properties. This study highlights the importance of growth-induced polarity imbalance during nanoparticle formation. This factor is often overlooked in interpretations that focus only on surface and volume energy contributions.

This phenomenon may also be associated with the formation of other metal oxides, such as those based on titanium, iron, and copper. While the authors acknowledge that the adopted model represents an approximation of a complex interaction and that future studies could benefit from more sophisticated approaches, accurately modeling both polar and nonpolar surfaces at a reasonable computational cost was successfully addressed by their chosen methodology.