Automated Author ProfileDallin, Bradley
University of Wisconsin–Madison0000-0002-8580-2816
Dallin, Bradley
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: 2.3 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Scripts, raw and processed data, Jupyter notebooks, and force field files for the simulations performed in: B. C. Dallin, A. S. Kelkar, and R. C. Van Lehn. “Structural Features of Interfacial Water Predict the Hydrophobicity of Chemically Heterogeneous Surfaces.” Chemical Science 2023.
Authors
- Dallin, Bradley C. ;
- Kelkar, Atharva S. ;
- Van Lehn, Reid C.
Scripts, raw and processed data, Jupyter notebooks, and force field files for the simulations performed in: B. C. Dallin, A. S. Kelkar, and R. C. Van Lehn. “Structural Features of Interfacial Water Predict the Hydrophobicity of Chemically Heterogeneous Surfaces.” Chemical Science 2023.
Authors
- Dallin, Bradley C. ;
- Kelkar, Atharva S. ;
- Van Lehn, Reid C.
Hydrophobic interactions drive numerous biological and synthetic processes. The materials used in these processes often possess chemically heterogeneous surfaces that are characterized by diverse chemical groups positioned in close proximity at the nanoscale; examples include functionalized nanomaterials and biomolecules like proteins and peptides. Nonadditive contributions to the hydrophobicity of such surfaces depend on the chemical identities and spatial patterns of polar and nonpolar groups in ways that remain poorly understood. Here, we develop a dual-loop active learning framework that combines a fast, reduced-accuracy method (a convolutional neural network) with a slow, higher-accuracy method (molecular dynamics simulations with enhanced sampling) to efficiently predict the hydration free energy, a thermodynamic descriptor of hydrophobicity, for nearly 200,000 chemically heterogeneous self-assembled monolayers (SAMs). Analysis of this data set reveals that SAMs with distinct polar groups exhibit substantial variations in hydrophobicity as a function of their composition and patterning, but the clustering of nonpolar groups is a common signature of highly hydrophobic patterns. Further MD analysis relates such clustering to the perturbation of interfacial water structure. These results provide new insight into the influence of chemical heterogeneity on hydrophobicity via quantitative analysis of a large set of surfaces, enabled by the active learning approach. Paper title: Identifying Nonadditive Contributions to the Hydrophobicity of Chemically Heterogeneous Surfaces via Dual-Loop Active LearningAuthors: Atharva Kelkar, Bradley Dallin, Reid Van LehnDOI: doi.org/10.1063/5.0072385
Authors
- Kelkar, Atharva ;
- Dallin, Bradley ;
- Van lehn, Reid