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Name sGDML_Benzene_DFT_NC2018
Extended ID Benzene_DFT_NC2018_ChmielaSaucedaMullerTkatchenko__DS_q9y1aat05u42_0
Description The data used for training the DFT models were created running ab initio MD in the NVT ensemble using the Nosé-Hoover thermostat at 500 K during a 200 ps simulation with a resolution of 0.5 fs. Forces and energies were computed using all-electrons at the generalized gradient approximation level of theory with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional, treating van der Waals interactions with the Tkatchenko-Scheffler (TS) method. All calculations were performed with FHI-aims. The final training data was generated by subsampling the full trajectory under preservation of the Maxwell-Boltzmann distribution for the energies.
Authors Stefan Chmiela
Huziel E. Sauceda
Klaus-Robert Müller
Alexandre Tkatchenko
DOI 10.60732/18404d62

Cite as: Chmiela, S., Sauceda, H. E., Müller, K., and Tkatchenko, A. "sGDML Benzene DFT NC2018." ColabFit, 2023.
For other citation formats, see the DataCite Fabrica page for this dataset.
Elements C (50.0%)
H (50.0%)
Number of Data Objects 49,863
Number of Configurations 49,863
Number of Atoms 598,356
Configuration Sets by Name (None)
Configuration Sets by ID (None)
Data Objects Too many to display
ColabFit ID DS_q9y1aat05u42_0
Files colabfitspec.json

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