Competing nucleation pathways in nanocrystal formation

TL;DR

This study uses advanced machine learning and data-driven methods to explore competing nucleation pathways in zinc oxide nanocrystal formation, revealing how different pathways dominate at various supercooling levels.

Contribution

It introduces a novel simulation approach combining machine-learning force fields and data-driven analysis to resolve fundamental challenges in atomistic nucleation studies.

Findings

Different nucleation pathways compete depending on supercooling degree.

Machine-learning force fields effectively capture complex atomic interactions.

Simulation results align with experimental observations of zinc oxide crystallization.

Abstract

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.