Non-classical nucleation of zinc oxide from a physically-motivated machine-learning approach

TL;DR

This paper develops a machine-learning force field for zinc oxide to simulate non-classical nucleation pathways, revealing complex crystal formation mechanisms with atomistic detail.

Contribution

It introduces a physically-motivated machine-learning approach to accurately model zinc oxide interactions and observe non-classical nucleation pathways in atomistic simulations.

Findings

Identification of prenucleation clusters

Observation of two-step nucleation scenarios

Validation of non-classical pathways in complex materials

Abstract

Observing non-classical nucleation pathways remains challenging in simulations of complex materials with technological interests. This is because it requires very accurate force fields that can capture the whole complexity of their underlying interatomic interactions and an advanced structural analysis. Here, we first report the construction of a machine-learning force field for zinc oxide interactions using the Physical LassoLars Interaction Potentials approach which allows us to be predictive even for untrained structures. Then, we carried out freezing simulations from a liquid and observed the crystal formation with atomistic precision. Our results, which are analyzed using a data-driven approach based on bond order parameters, demonstrate the presence of both prenucleation clusters and two-step nucleation scenarios thus retrieving seminal predictions of non-classical nucleation pathways made on much simpler models.