Dataset

DeePMD_SE


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Name :
DeePMD_SE
Extended ID :
DeePMD_SE__Zhang-Han-Wang-Saidi-Car-E__DS_k85kj1kiekip_0
ColabFit ID :
DS_k85kj1kiekip_0
Files :
colabfitspec.json
Description :
127,000 configurations from a dataset used to benchmark and train a modified DeePMD model called DeepPot-SE, or Deep Potential - Smooth Edition
Authors :
Linfeng Zhang, Jiequn Han, Han Wang, Wissam A. Saidi, Roberto Car, Weinan E
DOI :
10.60732/d5518670 https://commons.datacite.org/doi.org/10.60732/d5518670 https://doi.datacite.org/dois/10.60732%2Fd5518670 https://doi.org/10.60732/d5518670 Cite as: Zhang, L., Han, J., Wang, H., Saidi, W. A., Car, R., and E, W. "DeePMD SE." ColabFit, 2023. https://doi.org/10.60732/d5518670.
For other citation formats, see the DataCite Fabrica page for this dataset.
Num. Configurations :
126,631
Num. Atoms :
26,210,897
Downloads :
96
Calculated Property Types :
atomic_forces cauchy_stress energy
Elements :
Al (2.06%) C (11.67%) Co (1.64%) Cr (1.64%) Cu (3.17%) Fe (1.64%) Ge (7.36%) H (11.67%) Mn (1.64%) Mo (5.96%) N (2.33%) Ni (1.64%) O (12.87%) Pt (7.96%) S (11.93%) Si (9.93%) Ti (4.89%)
Methods :
DFT-PBE
Software :
CP2K Quantum ESPRESSO
Publication Link :
https://papers.nips.cc/paper_files/paper/2018/file/e2ad76f2326fbc6b56a45a56c59fafdb-Paper.pdf
Data Source Link :
https://www.aissquare.com/datasets/detail?pageType=datasets&name=deepmd-se-dataset
Other Links :
https://doi.org/10.48550/arXiv.1805.09003
Configuration Sets by Name :
Configuration Sets by ID :
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