Dataset

defected_phosphorene_ACS_2023


Download Dataset XYZ file

Name defected_phosphorene_ACS_2023
Extended ID defected_phosphorene_ACS_2023_KyvalaAngelettiFranchiniDellago__DS_k059wtxqsksu_0
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.
Authors Lukáš Kývala
Andrea Angeletti
Cesare Franchini
Christoph Dellago
DOI 10.60732/87b2341a
https://commons.datacite.org/doi.org/10.60732/87b2341a
https://doi.datacite.org/dois/10.60732%2F87b2341a
https://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.
For other citation formats, see the DataCite Fabrica page for this dataset.
Elements P (100.0%)
Number of Data Objects 5,091
Number of Configurations 5,091
Number of Atoms 722,311
Links https://doi.org/10.5281/zenodo.8421094
https://doi.org/10.1021/acs.jpcc.3c05713
Configuration Sets by Name (None)
Configuration Sets by ID (None)
Data Objects
ColabFit ID DS_k059wtxqsksu_0
Files colabfitspec.json

No uploaded content is transferred in ownership from the original creators to ColabFit. All content is distributed under the license specified by its contributor who has stated that he or she has the authority to share it under the specified license.