Dataset

SAIT_semiconductors_ACS_2023_SiN_train


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Name :
SAIT_semiconductors_ACS_2023_SiN_train
Extended ID :
SAIT_semiconductors_ACS_2023_SiN_train__Kim-Na-Kim-Cho-Kang-Lee-Choi-Kim-Lee-Kim__DS_5yfdgzb5zhgm_0
ColabFit ID :
DS_5yfdgzb5zhgm_0
Files :
colabfitspec.json
Description :
Training configurations from the SAIT_semiconductors_ACS_2023_SiN dataset. This dataset contains SiN, Si and N configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
Authors :
Geonu Kim, Byunggook Na, Gunhee Kim, Hyuntae Cho, Seung-Jin Kang, Hee Sun Lee, Saerom Choi, Heejae Kim, Seungwon Lee, Yongdeok Kim
DOI :
10.60732/dbe982a6 https://commons.datacite.org/doi.org/10.60732/dbe982a6 https://doi.datacite.org/dois/10.60732%2Fdbe982a6 https://doi.org/10.60732/dbe982a6 Cite as: Kim, G., Na, B., Kim, G., Cho, H., Kang, S., Lee, H. S., Choi, S., Kim, H., Lee, S., and Kim, Y. "SAIT semiconductors ACS 2023 SiN train." ColabFit, 2024. https://doi.org/10.60732/dbe982a6.
For other citation formats, see the DataCite Fabrica page for this dataset.
Num. Configurations :
22,494
Num. Atoms :
1,283,591
Downloads :
60
Calculated Property Types :
atomic_forces cauchy_stress energy
Elements :
N (50.59%) Si (49.41%)
Methods :
DFT-PBE
Software :
VASP
Publication Link :
https://openreview.net/forum?id=hr9Bd1A9Un
Data Source Link :
https://github.com/SAITPublic/MLFF-Framework
Configuration Sets by Name :
Configuration Sets by ID :
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