Dataset
UNEP_v1_2023_train
Download Dataset XYZ files Download Dataset Parquet files
| Name | UNEP_v1_2023_train |
|---|---|
| Extended ID | UNEP-v1_2023__Song-Zhao-Liu-Wang-Lindgren-Wang-Chen-Xu-Liang-Ying-Xu-Zhao-Shi-Wang-Lyu-Zeng-Liang-Dong-Sun-Chen-Zhang-Guo-Qian-Sun-Erhart-Ala-Nissila-Su-Fan__DS_14h4rvviya0k_0 |
| Description | The training set for UNEP-v1 (version 1 of Unified NeuroEvolution Potential), a model implemented in GPUMD. |
| Authors |
Keke Song Rui Zhao Jiahui Liu Yanzhou Wang Eric Lindgren Yong Wang Shunda Chen Ke Xu Ting Liang Penghua Ying Nan Xu Zhiqiang Zhao Jiuyang Shi Junjie Wang Shuang Lyu Zezhu Zeng Shirong Liang Haikuan Dong Ligang Sun Yue Chen Zhuhua Zhang Wanlin Guo Ping Qian Jian Sun Paul Erhart Tapio Ala-Nissila Yanjing Su Zheyong Fan |
| DOI |
10.60732/23c88dd7
https://commons.datacite.org/doi.org/10.60732/23c88dd7 https://doi.datacite.org/dois/10.60732%2F23c88dd7 https://doi.org/10.60732/23c88dd7 Cite as: Song, K., Zhao, R., Liu, J., Wang, Y., Lindgren, E., Wang, Y., Chen, S., Xu, K., Liang, T., Ying, P., Xu, N., Zhao, Z., Shi, J., Wang, J., Lyu, S., Zeng, Z., Liang, S., Dong, H., Sun, L., Chen, Y., Zhang, Z., Guo, W., Qian, P., Sun, J., Erhart, P., Ala-Nissila, T., Su, Y., and Fan, Z. "UNEP v1 2023 train." ColabFit, 2023. https://doi.org/10.60732/23c88dd7. For other citation formats, see the DataCite Fabrica page for this dataset. |
| Calculated Property Types |
atomic_forces cauchy_stress energy |
| Elements |
Ag (6.45%) Al (7.1%) Au (6.28%) Cr (5.99%) Cu (6.37%) Mg (5.9%) Mo (5.52%) Ni (6.11%) Pb (5.9%) Pd (6.16%) Pt (6.14%) Ta (6.2%) Ti (6.74%) V (6.06%) W (6.38%) Zr (6.71%) |
| Number of Configurations | 104,799 |
| Number of Atoms | 6,840,534 |
| Links |
https://zenodo.org/doi/10.5281/zenodo.10081676 https://doi.org/10.48550/arXiv.2311.04732 |
| Configuration Sets by Name | (None) |
| Configuration Sets by ID | (None) |
| Calculated Properties | |
| ColabFit ID | DS_14h4rvviya0k_0 |
| Files | colabfitspec.json |
No uploaded content is transferred in ownership from the original creators to ColabFit. All content is distributed under the license specified by its contributor who has stated that he or she has the authority to share it under the specified license.