Dataset

defected_phosphorene_ACS_2023


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Species content of dataset


Name :
defected_phosphorene_ACS_2023
Authors :
Lukáš Kývala, Andrea Angeletti, Cesare Franchini, Christoph Dellago
Description :
This dataset contains pristine monolayer phosphorene as well as structures with monovacancies which were used to train an artificial neural network (ANN) for use with a high-dimensional neural network potentials molecular dynamics (HDNNP-MD) simulation. The publication investigates the mechanism and rates of the processes of defect diffusion, as well as monovacancy-to-divacancy defect coalescence.
DOI :
https://doi.org/10.60732/87b2341a https://commons.datacite.org/doi.org/10.60732/87b2341a
Cite As :
Kývala, L., Angeletti, A., Franchini, C., and Dellago, C. "defected phosphorene ACS 2023." ColabFit, 2023. https://doi.org/10.60732/87b2341a.
ColabFit ID :
DS_k059wtxqsksu_0
Extended ID :
defected_phosphorene_ACS_2023__Kyvala-Angeletti-Franchini-Dellago__DS_k059wtxqsksu_0
Date Added :
2023-12-19
License :
CC-BY-4.0
Downloads :
31
Num. Configurations :
5,085
Num. Atoms :
722,033
Calculated Property Types :
atomic_forces energy
Elements :
P (100.0%)
Methods :
DFT-PBE
Software :
VASP
Publication Link :
https://doi.org/10.1021/acs.jpcc.3c05713
Data Source Link :
https://doi.org/10.5281/zenodo.8421094
Spec File :
colabfitspec.json
Configuration Sets by Name :
Configuration Sets by ID :
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