Article: Diffusion and Coalescence of Phosphorene Monovacancies Studied Using High-Dimensional Neural Network Potentials.
The journal of physical chemistry. C, Nanomaterials and interfaces
2023 Volume 127, Issue 49, Page(s) 23743–23751
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) ... ...
| 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. |
|---|---|
| Language | English |
| Publishing date | 2023-12-05 |
| Publishing country | United States |
| Document type | Journal Article |
| ISSN | 1932-7447 |
| ISSN | 1932-7447 |
| DOI | 10.1021/acs.jpcc.3c05713 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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