Automated Author ProfileYang, Yoona
Sandia National Laboratories0000-0002-7446-8816
Yang, Yoona
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: 1.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This is a supplementary information for our paper titled "Multifidelity neural network formulations for prediction of quantum chemistry potential energy surfaces" Supplemental information includes two data files corresponding to the complete sets of low and high fidelity training data used in numerical experiments. Format is JavaScript Object Notation (JSON). 1. low_fidelity_training_data.json contains 74000 records 2. high_fidelity_training_data.json contains 36988 records Each record consists of a numerical id ("id"), (x,y,z) position tuples ("geometry") for C5H5 ordered as 5 carbon atoms followed by 5 hydrogen atoms, and corresponding potential energy ("energy"). Source: normal mode sampling around 2 wells, 1 transition state, and a set of IRCs as depicted in Figure 2. Usage: subsets of this data were used as needed to define different data amounts and different subset randomizations in Figures 5 through 8.
Authors
- Yang, Yoona ;
- Eldred, Michael S. ;
- Zádor, Judit ;
- Najm, Habib N.
This is a supplementary information for our paper titled "Multifidelity neural network formulations for prediction of quantum chemistry potential energy surfaces" Supplemental information includes two data files corresponding to the complete sets of low and high fidelity training data used in numerical experiments. Format is JavaScript Object Notation (JSON). 1. low_fidelity_training_data.json contains 74000 records 2. high_fidelity_training_data.json contains 36988 records Each record consists of a numerical id ("id"), (x,y,z) position tuples ("geometry") for C5H5 ordered as 5 carbon atoms followed by 5 hydrogen atoms, and corresponding potential energy ("energy"). Source: normal mode sampling around 2 wells, 1 transition state, and a set of IRCs as depicted in Figure 2. Usage: subsets of this data were used as needed to define different data amounts and different subset randomizations in Figures 5 through 8.
Authors
- Yang, Yoona ;
- Eldred, Michael S. ;
- Zádor, Judit ;
- Najm, Habib N.