Dataset
defected_phosphorene_ACS_2023
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| Name | defected_phosphorene_ACS_2023 |
|---|---|
| Extended ID | defected_phosphorene_ACS_2023__Kývala-Angeletti-Franchini-Dellago__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. |
| Calculated Property Types |
atomic_forces cauchy_stress energy |
| Elements |
P (100.0%) |
| 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) |
| Calculated Properties | |
| ColabFit ID | DS_k059wtxqsksu_0 |
| Files | colabfitspec.json |
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