Dataset

defected_phosphorene_ACS_2023

Download Original Data Files 24.1 MB

Download Dataset Parquet Files 20.2 MB

Download Dataset XYZ Files 14.8 MB

Find on Hugging Face 🤗

Species content of dataset

Dataset viewer powered by Hugging Face

Name :

defected_phosphorene_ACS_2023

Extended ID :

defected_phosphorene_ACS_2023__Kyvala-Angeletti-Franchini-Dellago__DS_k059wtxqsksu_0

ColabFit ID :

DS_k059wtxqsksu_0

Files :

colabfitspec.json

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.

Num. Configurations :

5,085

Num. Atoms :

722,033

Downloads :

Calculated Property Types :

atomic_forces cauchy_stress 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

Configuration Sets by Name :

Configuration Sets by ID :

Report an issue with this dataset

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.