Sukriti Manna
- Materials Chemistry top 10%
- Electrical and Electronic Engineering top 10%
- Biomedical Engineering
- Mechanical Engineering top 10%
- Polymers and Plastics top 10%
- Co-authors
- Subramanian K. R. S. SankaranarayananCristian V. CiobanuMichael SternbergRasoul KhaledialidustiZachary D. HoodBabak AnasoriSrinivasa Kartik NemaniBrian C. Wyatt
- Topics
- Machine Learning in Materials Science (18 papers)Advanced Memory and Neural Computing (8 papers)Computational Drug Discovery Methods (5 papers)
- Partner nations
- United StatesIndiaChina
In The Last Decade
Sukriti Manna
43 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 66
- Materials Chemistry 725
- Electrical and Electronic Engineering 446
- Biomedical Engineering 211
- Mechanical Engineering 163
- Polymers and Plastics 108
Countries citing papers authored by Sukriti Manna
This map shows the geographic impact of Sukriti Manna'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 Sukriti Manna with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sukriti Manna more than expected).
Fields of papers citing papers by Sukriti Manna
This network shows the impact of papers produced by Sukriti Manna. 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 Sukriti Manna. The network helps show where Sukriti Manna may publish in the future.
Co-authorship network of co-authors of Sukriti Manna
This figure shows the co-authorship network connecting the top 25 collaborators of Sukriti Manna. A scholar is included among the top collaborators of Sukriti Manna 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 Sukriti Manna. Sukriti Manna is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 4 | |
| 5 | 3 | |
| 6 | 9 | |
| 7 | 7 | |
| 8 | 3 | |
| 9 | 5 | |
| 10 | 38 | |
| 11 | 16 | |
| 12 | 33 | |
| 13 | 21 | |
| 14 | 2 | |
| 15 | 22 | |
| 16 | 41 | |
| 17 | 6 | |
| 18 | 18 | |
| 19 | 166 | |
| 20 | High-Entropy 2D Carbide MXenes: TiVNbMoC 3 and TiVCrMoC 3breakdown → | 292 |
About Sukriti Manna
Sukriti Manna is a scholar working on Materials Chemistry, Ceramics and Composites and Polymers and Plastics, having authored 43 papers that have together received 1.1k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (18 papers), Advanced Memory and Neural Computing (8 papers) and Computational Drug Discovery Methods (5 papers). The work is most often cited by research in Materials Chemistry (725 citations), Condensed Matter Physics (97 citations) and Electrical and Electronic Engineering (446 citations). Sukriti Manna has collaborated with scholars based in United States, India and China. Frequent co-authors include Subramanian K. R. S. Sankaranarayanan, Cristian V. Ciobanu, Michael Sternberg, Rasoul Khaledialidusti, Zachary D. Hood, Babak Anasori, Srinivasa Kartik Nemani, Brian C. Wyatt, Weichen Hong and Tamoghna Chakrabarti. Their work appears in journals such as Science, Advanced Materials 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.