Dataset
NequIP-NC-2022
Download Dataset XYZ file
| Name | NequIP-NC-2022 |
|---|---|
| Extended ID | NequIP-NC-2022_BatznerMusaelianSunGeigerMailoaKornbluthMolinariSmidtKozinsky__DS_sipksdvyrxsf_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 |
| Elements |
Li (29.82%) O (36.88%) P (14.42%) S (14.42%) C (0.09%) Cu (4.29%) H (0.09%) |
| Number of Data Objects | 56,824 |
| Number of Configurations | 56,824 |
| Number of Atoms | 7,629,879 |
| Links |
https://doi.org/10.24435/materialscloud:s0-5n https://doi.org/10.1038/s41467-022-29939-5 |
| 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_ronv5utc1m71_0 CS_rlxzvmmm4jfw_0 CS_tgj1j5vv0vkc_0 |
| Data Objects | Too many to display |
| ColabFit ID | DS_sipksdvyrxsf_0 |
| Files | colabfitspec.json |
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