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  Nonclassical Nucleation of Zinc Oxide from a Physically Motivated Machine-Learning Approach

Goniakowski, J., Menon, S., Laurens, G., & Lam, J. (2022). Nonclassical Nucleation of Zinc Oxide from a Physically Motivated Machine-Learning Approach. The Journal of Physical Chemistry C, 126(40), 17456-17469. doi:10.1021/acs.jpcc.2c06341.

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Goniakowski, Jacek1, Author
Menon, Sarath2, Author           
Laurens, Gaétan3, Author
Lam, Julien4, Author
Affiliations:
1CNRS, Sorbonne Université, Institut des NanoSciences de Paris, UMR 7588, 4 Place Jussieu, F-75005 Paris, France, ou_persistent22              
2Computational Phase Studies, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863341              
3Institut Lumière Matière, UMR5306 Université Lyon 1-CNRS, Université de Lyon, 69622 Villeurbanne Cedex, France, ou_persistent22              
4Centre d’élaboration des Matériaux et d’Etudes Structurales, CNRS (UPR 8011), 29 rue Jeanne Marvig, 31055 Toulouse Cedex 4, France, ou_persistent22              

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 Abstract: Observing nonclassical 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. Here, we first report the construction and particularly thorough validation 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 high-temperature dynamical systems such as ZnO melt. Then, we carried out several types of crystallization simulations and followed the formation of ZnO crystals 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 nonclassical 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 predicted nonclassical nucleation scenarios.

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Language(s): eng - English
 Dates: 2022-09-28
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1021/acs.jpcc.2c06341
 Degree: -

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Title: The Journal of Physical Chemistry C
  Abbreviation : J. Phys. Chem. C
Source Genre: Journal
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Publ. Info: Washington, D.C. : American Chemical Society
Pages: - Volume / Issue: 126 (40) Sequence Number: - Start / End Page: 17456 - 17469 Identifier: ISSN: 1932-7447
CoNE: https://pure.mpg.de/cone/journals/resource/954926947766