Diffusion and Coalescence of Phosphorene Monovacancies Studied Using High-Dimensional Neural Network Potentials

Autor(en)

Lukas Kyvala, Andrea Angeletti, Cesare Franchini, Christoph Dellago

Abstrakt

The properties of two-dimensional materials are strongly affected by defects that are often present in considerable numbers. In this study, we investigate the diffusion and coalescence of monovacancies in phosphorene using molecular dynamics (MD) simulations accelerated by high-dimensional neural network potentials. Trained and validated with reference data obtained with density functional theory (DFT), such surrogate models provide the accuracy of DFT at a much lower cost, enabling simulations on time scales that far exceed those of first-principles MD. Our microsecond long simulations reveal that monovacancies are highly mobile and move predominantly in the zigzag rather than armchair direction, consistent with the energy barriers of the underlying hopping mechanisms. In further simulations, we find that monovacancies merge into energetically more stable and less mobile divacancies following different routes that may involve metastable intermediates.

Organisation(en)

Computergestützte Physik und Physik der Weichen Materie, Computergestützte Materialphysik

Externe Organisation(en)

Università di Bologna

Journal

Journal of Physical Chemistry C

Band

127

Seiten

23743-23751

Anzahl der Seiten

9

ISSN

1932-7447

DOI

https://doi.org/10.1021/acs.jpcc.3c05713

Publikationsdatum

12-2023

Peer-reviewed

Ja

ÖFOS 2012

103043 Computational Physics

ASJC Scopus Sachgebiete

Electronic, Optical and Magnetic Materials, Allgemeine Energie, Surfaces, Coatings and Films, Physical and Theoretical Chemistry

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/7738a30d-c290-486e-85b6-4828b79d6d9b