Francesca Tavazza
- Materials Chemistry top 2%
- Electrical and Electronic Engineering top 5%
- Atomic and Molecular Physics, and Optics top 5%
- Biomedical Engineering top 10%
- Mechanical Engineering top 5%
- Co-authors
- Kamal ChoudharyAnkit AgrawalAlok ChoudharyBrian DeCostChandler A. BeckerKevin F. GarrityZachary TrauttIrina Kalish
- Topics
- Machine Learning in Materials Science (25 papers)Surface and Thin Film Phenomena (20 papers)2D Materials and Applications (17 papers)
- Partner nations
- United StatesItalySwitzerland
In The Last Decade
Francesca Tavazza
80 papers receiving 3.6k citations
Hit Papers
Peers
Comparison fields: 5 of 136
- Materials Chemistry 2.6k
- Electrical and Electronic Engineering 934
- Atomic and Molecular Physics, and Optics 727
- Biomedical Engineering 420
- Mechanical Engineering 411
Countries citing papers authored by Francesca Tavazza
This map shows the geographic impact of Francesca Tavazza'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 Francesca Tavazza with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesca Tavazza more than expected).
Fields of papers citing papers by Francesca Tavazza
This network shows the impact of papers produced by Francesca Tavazza. 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 Francesca Tavazza. The network helps show where Francesca Tavazza may publish in the future.
Co-authorship network of co-authors of Francesca Tavazza
This figure shows the co-authorship network connecting the top 25 collaborators of Francesca Tavazza. A scholar is included among the top collaborators of Francesca Tavazza 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 Francesca Tavazza. Francesca Tavazza is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 44 | |
| 2 | 12 | |
| 3 | 72 | |
| 4 | 23 | |
| 5 | 20 | |
| 6 | Recent advances and applications of deep learning methods in materials sciencebreakdown → | 652 |
| 7 | 109 | |
| 8 | 27 | |
| 9 | 45 | |
| 10 | 33 | |
| 11 | 106 | |
| 12 | 237 | |
| 13 | Probing the Dielectric Response of the Interfacial Buffer Layer in Epitaxial Graphene via Optical Spectroscopy | 1 |
| 14 | 118 | |
| 15 | 16 | |
| 16 | 67 | |
| 17 | 4 | |
| 18 | 1 | |
| 19 | 7 | |
| 20 | 9 |
About Francesca Tavazza
Francesca Tavazza is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics and Structural Biology, having authored 81 papers that have together received 3.7k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (25 papers), Surface and Thin Film Phenomena (20 papers) and 2D Materials and Applications (17 papers). The work is most often cited by research in Materials Chemistry (2.6k citations), Structural Biology (54 citations) and Metals and Alloys (77 citations). Francesca Tavazza has collaborated with scholars based in United States, Italy and Switzerland. Frequent co-authors include Kamal Choudhary, Ankit Agrawal, Alok Choudhary, Brian DeCost, Chandler A. Becker, Kevin F. Garrity, Zachary Trautt, Irina Kalish, Ryan Beams and Lyle E. Levine. Their work appears in journals such as Science, Physical Review Letters and Nature Communications.
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.