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Name NequIP_NC_2022
Extended ID NequIP_NC_2022_BatznerMusaelianSunGeigerMailoaKornbluthMolinariSmidtKozinsky__DS_4pbhjtu62o2d_0
Description Approximately 57,000 configurations from the evaluation datasets for NequIP graph neural network model for interatomic potentials. Trajectories have been taken from LIPS, LIPO glass melt-quench simulation, and formate decomposition on Cu datasets.
Authors Simon Batzner
Albert Musaelian
Lixin Sun
Mario Geiger
Jonathan P. Mailoa
Mordechai Kornbluth
Nicola Molinari
Tess E. Smidt
Boris Kozinsky
DOI 10.60732/e05d99fd

Cite as: Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., and Kozinsky, B. "NequIP NC 2022." ColabFit, 2023.
For other citation formats, see the DataCite Fabrica page for this dataset.
Elements Li (29.81%)
O (36.87%)
P (14.41%)
S (14.42%)
C (0.09%)
Cu (4.31%)
H (0.09%)
Number of Data Objects 56,856
Number of Configurations 56,856
Number of Atoms 7,631,075
Configuration Sets by Name NequIP_NC_2022_LIPS — Lithium Thiophosphate (Li6.75P3S11) configurations from NequIP_NC_2022 dataset
NequIP_NC_2022_LIPO_quench — Lithium Phosphate amorphous glass (Li4P2O7) configurations from NequIP_NC_2022 dataset
NequIP_NC_2022_Cu_formate — Cu-formate configurations, Cu <110> undergoing dehydrogenation decomposition, from NequIP_NC_2022 dataset
Configuration Sets by ID CS_g122der0lmc1_0
Data Objects Too many to display
ColabFit ID DS_4pbhjtu62o2d_0
Files colabfitspec.json

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