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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

Cite as: Kývala, L., Angeletti, A., Franchini, C., and Dellago, C. "defected phosphorene ACS 2023." ColabFit, 2023.
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
Configuration Sets by Name (None)
Configuration Sets by ID (None)
Data Objects
ColabFit ID DS_k059wtxqsksu_0
Files colabfitspec.json

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