Diffusion and Coalescence of Phosphorene Monovacancies Studied Using High-Dimensional Neural Network Potentials
Lukáš Kývala, Andrea Angeletti, Cesare Franchini, Christoph Dellago · 2023
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Description
Lukáš Kývala and three co-authors simulate how single missing atoms wander and merge in phosphorene, a one-atom-thick phosphorus sheet. Their study appeared in twenty twenty-three. Defects shape what two-dimensional materials can do, and they are usually plentiful.
Direct quantum simulations are too slow to watch defects move for long. The team trained neural network models on quantum data, keeping near-quantum accuracy at a fraction of the cost. Their microsecond-long runs show single vacancies racing along the zigzag direction of the sheet, not the crosswise one. That matches the energy barriers for each hop. Vacancies that meet fuse into double vacancies, which sit more stably and barely move. Several merging paths turned up, some with short-lived middle forms.
Catalog record
| CREATOR | Lukáš Kývala, Andrea Angeletti, Cesare Franchini, Christoph Dellago |
|---|---|
| YEAR | 2023 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 30 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |