Automated Author ProfileZheng, Ruiheng
State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, People's Republic of China.
Zheng, Ruiheng
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.4 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This study combines machine learning (ML) and high-throughput calculations to uncover new ternary electrides in the A₂BC₂ family of compounds with the P4/mbm space group. Starting from a library of 214 known A₂BC₂ phases, density-functional theory calculations were used to compute the maximum value of the electron localization function, indicating that 42 are potential electrides. A model was then trained on this dataset and used to predict the electride behaviour of 14,437 hypothetical compounds generated by structural prototyping. Then, the stability and electride features of the 1254 electride candidates predicted by the model were carefully checked by high-throughput calculations.
Authors
- Wang, Zhiqi ;
- Gong, Yutong ;
- Evans, Matthew L. ;
- Yan, Yujing ;
- Wang, Shiyao ;
- Miao, Nanxi ;
- Zheng, Ruiheng ;
- Rignanese, Gian-Marco ;
- Wang, Junjie
This study combines machine learning (ML) and high-throughput calculations to uncover new ternary electrides in the A₂BC₂ family of compounds with the P4/mbm space group. Starting from a library of 214 known A₂BC₂ phases, density-functional theory calculations were used to compute the maximum value of the electron localization function, indicating that 42 are potential electrides. A model was then trained on this dataset and used to predict the electride behaviour of 14,437 hypothetical compounds generated by structural prototyping. Then, the stability and electride features of the 1254 electride candidates predicted by the model were carefully checked by high-throughput calculations.
Authors
- Wang, Zhiqi ;
- Gong, Yutong ;
- Evans, Matthew L. ;
- Yan, Yujing ;
- Wang, Shiyao ;
- Miao, Nanxi ;
- Zheng, Ruiheng ;
- Rignanese, Gian-Marco ;
- Wang, Junjie