• Corpus ID: 237279178

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

@inproceedings{Laurens2021NonclassicalNO,
  title={Non-classical nucleation of zinc oxide from a physically-motivated machine-learning approach},
  author={Ga{\'e}tan Laurens and Jacek Goniakowski and Julien Lam},
  year={2021}
}

• G. Laurens, J. Goniakowski, Julien Lam
• Published 24 August 2021
• Materials Science

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 able to discriminate between competing crystalline phases. HereWe first describe how we used the Physical LassoLars Interaction Potentials technique to create a machine-learning force field for zinc oxide interactions. Then, we carried out several types of crystallization simulations and followed the formation of ZnO crystal with atomistic precision. Our results, which were 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. Dedicated calculations of high temperature ZnO free energy within a newly developed automated nonequilibrium thermodynamic integration method revealed the existence of a thermodynamic bias for the observed non-classical nucleation scenarios.