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Quentin Gromoff

Publications and source records attributed to Quentin Gromoff.

3 recordsLinked to original sources

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

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.

cond-mat.mtrl-sci

Competing nucleation pathways in nanocrystal formation

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.

cond-mat.stat-mech

Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations

While nanoalloys are of paramount scientific and practical interests, the main processes leading to their formation are still poorly understood. Key structural features in the alloy systems, including crystal phase, chemical ordering, and morphology, are challenging to control at the nanoscale, making it difficult to transfer their usage to industrial applications. In this contribution, we focus on the gold/silver system that has two of the most prevalent noble metals, and combine experiments with simulations to uncover the formation mechanisms at the atomic-level. Nanoparticles are produced using state-of-the-art inert-gas aggregation source and analyzed using transmission electron microscopy and energy-dispersive x-ray spectroscopy. Machine-learning-assisted molecular dynamics simulations are employed to model the crystallization process from liquid droplets to nanocrystals. Our study finds a preponderance of nanoparticles with five-fold symmetric morphology, including icosahedron and decahedron which is consistent with previous results on mono-metallic nanoparticles. However, we observe that gold atoms, rather than silver atoms, segregate at the surface of the obtained nanoparticles for all the considered alloy compositions. These segregation tendencies are in contrast to previous studies and have consequences on the crystallization dynamics and the subsequent crystal ordering. We finally show that the underpinnings of this surprising segregation dynamics is due to charge transfer and electrostatic interactions rather than surface energy considerations.

cond-mat.mtrl-sci