Vishnu Sresht

1.6k total citations
22 papers, 1.2k citations indexed

About

Vishnu Sresht is a scholar working on Materials Chemistry, Biomedical Engineering and Molecular Biology. According to data from OpenAlex, Vishnu Sresht has authored 22 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Materials Chemistry, 9 papers in Biomedical Engineering and 7 papers in Molecular Biology. Recurrent topics in Vishnu Sresht's work include Modular Robots and Swarm Intelligence (3 papers), Machine Learning in Materials Science (3 papers) and Computational Drug Discovery Methods (3 papers). Vishnu Sresht is often cited by papers focused on Modular Robots and Swarm Intelligence (3 papers), Machine Learning in Materials Science (3 papers) and Computational Drug Discovery Methods (3 papers). Vishnu Sresht collaborates with scholars based in United States, Germany and France. Vishnu Sresht's co-authors include Daniel Blankschtein, Agı́lio A. H. Pádua, Timothy M. Swager, Lauren D. Zarzar, Julia A. Kalow, Ellen M. Sletten, Michael S. Strano, Ananth Govind Rajan, Meihua Tu and Alan M. Mathiowetz and has published in prestigious journals such as Nature, Physical Review Letters and Nature Communications.

In The Last Decade

Vishnu Sresht

22 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vishnu Sresht United States 15 755 384 229 212 186 22 1.2k
Nastaran Meftahi Australia 18 554 0.7× 226 0.6× 140 0.6× 302 1.4× 63 0.3× 35 1.2k
Thomas E. Gartner United States 11 475 0.6× 190 0.5× 110 0.5× 137 0.6× 34 0.2× 27 886
Gabriel R. Schleder Brazil 19 967 1.3× 266 0.7× 120 0.5× 353 1.7× 116 0.6× 44 1.6k
Christopher R. Iacovella United States 24 826 1.1× 310 0.8× 329 1.4× 172 0.8× 23 0.1× 55 1.6k
Phillip W. Snyder United States 15 207 0.3× 445 1.2× 554 2.4× 78 0.4× 110 0.6× 22 1.4k
Mohammad Rahimi United States 23 653 0.9× 277 0.7× 249 1.1× 86 0.4× 21 0.1× 39 1.6k
Anastassia N. Rissanou Greece 17 557 0.7× 203 0.5× 254 1.1× 62 0.3× 39 0.2× 48 1.1k
Priti S. Mohanty India 20 645 0.9× 343 0.9× 95 0.4× 52 0.2× 28 0.2× 52 1.2k
Yushi Oishi Japan 20 381 0.5× 223 0.6× 343 1.5× 431 2.0× 46 0.2× 120 1.3k
Brian H. Morrow United States 21 414 0.5× 342 0.9× 275 1.2× 135 0.6× 33 0.2× 33 1.1k

Countries citing papers authored by Vishnu Sresht

Since Specialization
Citations

This map shows the geographic impact of Vishnu Sresht'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 Vishnu Sresht with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vishnu Sresht more than expected).

Fields of papers citing papers by Vishnu Sresht

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Vishnu Sresht. 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 Vishnu Sresht. The network helps show where Vishnu Sresht may publish in the future.

Co-authorship network of co-authors of Vishnu Sresht

This figure shows the co-authorship network connecting the top 25 collaborators of Vishnu Sresht. A scholar is included among the top collaborators of Vishnu Sresht 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 Vishnu Sresht. Vishnu Sresht 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.
Brajesh, K., Vishnu Sresht, Qingyi Yang, et al.. (2022). TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics. Journal of Chemical Information and Modeling. 62(4). 785–800. 32 indexed citations
2.
3.
Gao, Kaifu, Duc Duy Nguyen, Vishnu Sresht, et al.. (2020). Are 2D fingerprints still valuable for drug discovery?. Physical Chemistry Chemical Physics. 22(16). 8373–8390. 94 indexed citations
4.
Brajesh, K., Vishnu Sresht, Qingyi Yang, et al.. (2019). Comprehensive Assessment of Torsional Strain in Crystal Structures of Small Molecules and Protein–Ligand Complexes using ab Initio Calculations. Journal of Chemical Information and Modeling. 59(10). 4195–4208. 23 indexed citations
5.
Lee, Alpha A., Qingyi Yang, Vishnu Sresht, et al.. (2019). Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space. Chemical Communications. 55(81). 12152–12155. 89 indexed citations
6.
Nagelberg, Sara, Lauren D. Zarzar, Vishnu Sresht, et al.. (2018). Reconfigurable and dynamically tunable droplet-based compound micro-lenses. 3W3G.2–3W3G.2. 2 indexed citations
7.
Cho, H. Jeremy, Vishnu Sresht, & Evelyn N. Wang. (2018). Predicting Surface Tensions of Surfactant Solutions from Statistical Mechanics. Langmuir. 34(6). 2386–2395. 12 indexed citations
8.
Nagelberg, Sara, Lauren D. Zarzar, Natalie J. Nicolas, et al.. (2017). Reconfigurable and responsive droplet-based compound micro-lenses. Nature Communications. 8(1). 14673–14673. 134 indexed citations
9.
Brown, Paul, Vishnu Sresht, Hüseyin Burak Eral, et al.. (2017). CO2-Reactive Ionic Liquid Surfactants for the Control of Colloidal Morphology. Langmuir. 33(31). 7633–7641. 4 indexed citations
10.
Sresht, Vishnu, Eric P. Lewandowski, Daniel Blankschtein, & Arben Jusufi. (2017). Combined Molecular Dynamics Simulation–Molecular-Thermodynamic Theory Framework for Predicting Surface Tensions. Langmuir. 33(33). 8319–8329. 46 indexed citations
11.
Sresht, Vishnu, Ananth Govind Rajan, Émilie Bordes, et al.. (2017). Quantitative Modeling of MoS2–Solvent Interfaces: Predicting Contact Angles and Exfoliation Performance using Molecular Dynamics. The Journal of Physical Chemistry C. 121(16). 9022–9031. 89 indexed citations
12.
Rajan, Ananth Govind, Vishnu Sresht, Agı́lio A. H. Pádua, Michael S. Strano, & Daniel Blankschtein. (2016). Dominance of Dispersion Interactions and Entropy over Electrostatics in Determining the Wettability and Friction of Two-Dimensional MoS2 Surfaces. ACS Nano. 10(10). 9145–9155. 63 indexed citations
13.
Lin, Shangchao, Chih‐Jen Shih, Vishnu Sresht, et al.. (2016). Understanding the colloidal dispersion stability of 1D and 2D materials: Perspectives from molecular simulations and theoretical modeling. Advances in Colloid and Interface Science. 244. 36–53. 46 indexed citations
14.
Zarzar, Lauren D., Vishnu Sresht, Ellen M. Sletten, et al.. (2015). Dynamically reconfigurable complex emulsions via tunable interfacial tensions. Nature. 518(7540). 520–524. 353 indexed citations
15.
Sresht, Vishnu, Agı́lio A. H. Pádua, & Daniel Blankschtein. (2015). Liquid-Phase Exfoliation of Phosphorene: Design Rules from Molecular Dynamics Simulations. ACS Nano. 9(8). 8255–8268. 160 indexed citations
16.
Sresht, Vishnu, et al.. (2014). Understanding Miltefosine-Membrane Interactions using Molecular Dynamics Simulations. Biophysical Journal. 106(2). 802a–802a. 14 indexed citations
17.
Ulissi, Zachary W., Jingqing Zhang, Vishnu Sresht, Daniel Blankschtein, & Michael S. Strano. (2014). 2D Equation-of-State Model for Corona Phase Molecular Recognition on Single-Walled Carbon Nanotube and Graphene Surfaces. Langmuir. 31(1). 628–636. 24 indexed citations
18.
Cho, H. Jeremy, Vishnu Sresht, Daniel Blankschtein, & Evelyn N. Wang. (2013). Understanding Enhanced Boiling With Triton X Surfactants. 6 indexed citations
19.
Sresht, Vishnu, et al.. (2013). Understanding Enhanced Boiling With Triton X Surfactants. DSpace@MIT (Massachusetts Institute of Technology). 2 indexed citations
20.
Sresht, Vishnu, Jayesh Bellare, & Santosh K. Gupta. (2011). Modeling the Cytotoxicity of Cisplatin. Industrial & Engineering Chemistry Research. 50(23). 12872–12880. 6 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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