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

Lukas Kyvala (Corresponding author), Andrea Angeletti, Cesare Franchini, Christoph Dellago (Corresponding author)

Publications: Contribution to journalArticlePeer Reviewed

Abstract

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.

Original languageEnglish
Pages (from-to)23743-23751
Number of pages9
JournalJournal of Physical Chemistry C
Volume127
Issue number49
DOIs
Publication statusPublished - 14 Dec 2023

Austrian Fields of Science 2012

  • 103043 Computational physics

Fingerprint

Dive into the research topics of 'Diffusion and Coalescence of Phosphorene Monovacancies Studied Using High-Dimensional Neural Network Potentials'. Together they form a unique fingerprint.

Cite this