Jenna A. Bilbrey

707 total citations
21 papers, 535 citations indexed

About

Jenna A. Bilbrey is a scholar working on Materials Chemistry, Organic Chemistry and Electrical and Electronic Engineering. According to data from OpenAlex, Jenna A. Bilbrey has authored 21 papers receiving a total of 535 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Materials Chemistry, 6 papers in Organic Chemistry and 6 papers in Electrical and Electronic Engineering. Recurrent topics in Jenna A. Bilbrey's work include Machine Learning in Materials Science (8 papers), Molecular Junctions and Nanostructures (4 papers) and Protein Structure and Dynamics (3 papers). Jenna A. Bilbrey is often cited by papers focused on Machine Learning in Materials Science (8 papers), Molecular Junctions and Nanostructures (4 papers) and Protein Structure and Dynamics (3 papers). Jenna A. Bilbrey collaborates with scholars based in United States and India. Jenna A. Bilbrey's co-authors include Jason Locklin, Wesley D. Allen, Evan M. White, Jeremy Yatvin, Gareth R. Sheppard, S. Kyle Sontag, Kristen H. Fries, Neeraj Kumar, Malachi Schram and Sutanay Choudhury and has published in prestigious journals such as The Journal of Chemical Physics, Langmuir and Scientific Reports.

In The Last Decade

Jenna A. Bilbrey

21 papers receiving 529 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jenna A. Bilbrey United States 11 218 118 102 97 83 21 535
Hai‐Liang Ni China 15 428 2.0× 233 2.0× 133 1.3× 135 1.4× 62 0.7× 57 746
Mrigendra Dubey India 18 239 1.1× 348 2.9× 73 0.7× 72 0.7× 139 1.7× 45 771
Stefan Schmatloch Netherlands 13 323 1.5× 204 1.7× 81 0.8× 75 0.8× 132 1.6× 22 573
Hiroaki Shimomoto Japan 19 658 3.0× 115 1.0× 121 1.2× 45 0.5× 71 0.9× 49 839
Zhiyong Zhao China 17 343 1.6× 258 2.2× 70 0.7× 81 0.8× 44 0.5× 43 759
Jerald E. Hertzog United States 6 291 1.3× 156 1.3× 65 0.6× 20 0.2× 31 0.4× 10 483
Koji Yamauchi Japan 10 424 1.9× 186 1.6× 61 0.6× 83 0.9× 32 0.4× 12 703
Josephine L. Harries United Kingdom 11 237 1.1× 174 1.5× 82 0.8× 40 0.4× 40 0.5× 21 494
Anastasia Meristoudi Greece 12 116 0.5× 117 1.0× 82 0.8× 48 0.5× 24 0.3× 28 363
Benjamin W. Rawe Canada 13 643 2.9× 271 2.3× 72 0.7× 45 0.5× 300 3.6× 23 881

Countries citing papers authored by Jenna A. Bilbrey

Since Specialization
Citations

This map shows the geographic impact of Jenna A. Bilbrey's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jenna A. Bilbrey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jenna A. Bilbrey more than expected).

Fields of papers citing papers by Jenna A. Bilbrey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jenna A. Bilbrey. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jenna A. Bilbrey. The network helps show where Jenna A. Bilbrey may publish in the future.

Co-authorship network of co-authors of Jenna A. Bilbrey

This figure shows the co-authorship network connecting the top 25 collaborators of Jenna A. Bilbrey. A scholar is included among the top collaborators of Jenna A. Bilbrey based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jenna A. Bilbrey. Jenna A. Bilbrey is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Bilbrey, Jenna A., et al.. (2025). Uncertainty quantification for neural network potential foundation models. npj Computational Materials. 11(1). 6 indexed citations
2.
Bilbrey, Jenna A., et al.. (2024). Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity Graph Clustering. Microscopy and Microanalysis. 30(4). 741–750. 1 indexed citations
3.
Bilbrey, Jenna A., Mario Michael Krell, Tom Murray, et al.. (2024). Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units. Journal of Chemical Information and Modeling. 64(5). 1568–1580. 2 indexed citations
4.
Prange, Micah P., et al.. (2023). An open database of computed bulk ternary transition metal dichalcogenides. Scientific Data. 10(1). 336–336. 6 indexed citations
5.
Bilbrey, Jenna A., et al.. (2023). Active sampling for neural network potentials: Accelerated simulations of shear-induced deformation in Cu–Ni multilayers. The Journal of Chemical Physics. 158(11). 114103–114103. 3 indexed citations
6.
Bilbrey, Jenna A., Nanjun Chen, Shenyang Hu, & Peter V. Sushko. (2022). Graph-component approach to defect identification in large atomistic simulations. Computational Materials Science. 214. 111700–111700. 1 indexed citations
7.
Bilbrey, Jenna A., et al.. (2022). Decoding the protein–ligand interactions using parallel graph neural networks. Scientific Reports. 12(1). 7624–7624. 26 indexed citations
8.
Bilbrey, Jenna A., Joseph P. Heindel, Malachi Schram, et al.. (2020). A look inside the black box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters. The Journal of Chemical Physics. 153(2). 24302–24302. 17 indexed citations
9.
Bilbrey, Jenna A., et al.. (2020). Improving radiograph analysis throughput through transfer learning and object detection. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 3. 9–9. 1 indexed citations
10.
Bilbrey, Jenna A., Carlos Ortiz Marrero, Michel Sassi, et al.. (2020). Tracking the Chemical Evolution of Iodine Species Using Recurrent Neural Networks. ACS Omega. 5(9). 4588–4594. 8 indexed citations
11.
Bilbrey, Jenna A., et al.. (2017). Ring-Walking of Zerovalent Nickel on Aryl Halides. Journal of Chemical Theory and Computation. 13(4). 1706–1711. 20 indexed citations
12.
Bilbrey, Jenna A., et al.. (2015). Functionalization of Reactive End Groups in Surface‐Initiated Kumada Catalyst‐Transfer Polycondensation. Macromolecular Symposia. 351(1). 27–36. 3 indexed citations
13.
14.
Gao, Jing, et al.. (2014). Rapid Electrochemical Reduction of Ni(II) Generates Reactive Monolayers for Conjugated Polymer Brushes in One Step. Langmuir. 30(34). 10465–10470. 6 indexed citations
15.
Sontag, S. Kyle, et al.. (2014). π-Complexation in Nickel-Catalyzed Cross-Coupling Reactions. The Journal of Organic Chemistry. 79(4). 1836–1841. 31 indexed citations
16.
Bilbrey, Jenna A., et al.. (2013). Exact ligand cone angles. Journal of Computational Chemistry. 34(14). 1189–1197. 119 indexed citations
17.
Fries, Kristen H., Gareth R. Sheppard, Jenna A. Bilbrey, & Jason Locklin. (2013). Tuning chelating groups and comonomers in spiropyran-containing copolymer thin films for color-specific metal ion binding. Polymer Chemistry. 5(6). 2094–2094. 33 indexed citations
18.
Bilbrey, Jenna A., et al.. (2013). Exact Ligand Solid Angles. Journal of Chemical Theory and Computation. 9(12). 5734–5744. 24 indexed citations
19.
Sontag, S. Kyle, et al.. (2012). Palladium‐Mediated Surface‐Initiated Kumada Catalyst Polycondensation: A Facile Route Towards Oriented Conjugated Polymers. Macromolecular Rapid Communications. 33(24). 2115–2120. 40 indexed citations
20.
Bilbrey, Jenna A., et al.. (2012). On the Role of Disproportionation Energy in Kumada Catalyst-Transfer Polycondensation. ACS Macro Letters. 1(8). 995–1000. 28 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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