Vikram Jadhao

745 total citations
34 papers, 494 citations indexed

About

Vikram Jadhao is a scholar working on Biomedical Engineering, Materials Chemistry and Physical and Theoretical Chemistry. According to data from OpenAlex, Vikram Jadhao has authored 34 papers receiving a total of 494 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Biomedical Engineering, 10 papers in Materials Chemistry and 8 papers in Physical and Theoretical Chemistry. Recurrent topics in Vikram Jadhao's work include Electrostatics and Colloid Interactions (8 papers), Machine Learning in Materials Science (4 papers) and Spectroscopy and Quantum Chemical Studies (4 papers). Vikram Jadhao is often cited by papers focused on Electrostatics and Colloid Interactions (8 papers), Machine Learning in Materials Science (4 papers) and Spectroscopy and Quantum Chemical Studies (4 papers). Vikram Jadhao collaborates with scholars based in United States, United Kingdom and Netherlands. Vikram Jadhao's co-authors include Mónica Olvera de la Cruz, Mark O. Robbins, Francisco J. Solis, Nancy Makri, Geoffrey Fox, Jos W. Zwanikken, Trevor Douglas, D. Bahadur, C. N. R. Rao and G. Rama Rao and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and The Journal of Chemical Physics.

In The Last Decade

Vikram Jadhao

29 papers receiving 487 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Vikram Jadhao United States	13	163	141	139	128	71	34	494
Mohamed Daoud France	12	252 1.5×	95 0.7×	61 0.4×	58 0.5×	26 0.4×	22	585
Kazem V. Edmond United States	13	431 2.6×	201 1.4×	45 0.3×	99 0.8×	31 0.4×	22	631
Pai‐Yi Hsiao Taiwan	15	139 0.9×	251 1.8×	174 1.3×	136 1.1×	72 1.0×	59	645
Yoav Tsori Israel	17	436 2.7×	218 1.5×	114 0.8×	138 1.1×	25 0.4×	49	763
Susumu Fujiwara Japan	14	364 2.2×	100 0.7×	65 0.5×	109 0.9×	24 0.3×	71	746
J. F. Joanny France	15	273 1.7×	170 1.2×	134 1.0×	190 1.5×	31 0.4×	21	874
Alessandro Patti United Kingdom	19	560 3.4×	105 0.7×	29 0.2×	63 0.5×	58 0.8×	53	803
B. L. Carvalho United States	7	324 2.0×	93 0.7×	43 0.3×	93 0.7×	39 0.5×	10	488
S. P. Meeker United Kingdom	8	472 2.9×	150 1.1×	54 0.4×	136 1.1×	57 0.8×	10	743
J. Thoen Belgium	11	191 1.2×	111 0.8×	37 0.3×	84 0.7×	15 0.2×	20	420

Countries citing papers authored by Vikram Jadhao

Since Specialization

Citations

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

Fields of papers citing papers by Vikram Jadhao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vikram Jadhao

This figure shows the co-authorship network connecting the top 25 collaborators of Vikram Jadhao. A scholar is included among the top collaborators of Vikram Jadhao 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 Vikram Jadhao. Vikram Jadhao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Uchida, Masaki, et al.. (2025). Multilayered ordered arrays self-assembled from a mixed population of nanoparticles. Soft Matter. 21(19). 3720–3740. 1 indexed citations

Wang, Linwei, et al.. (2025). ROSE: RADICAL Orchestrator for Surrogate Exploration. 61–70.

Sohrabpoor, Hamed, Wenhui Li, Alexander Gumennik, et al.. (2025). Industrial energy forecasting using dynamic attention neural networks. Energy and AI. 20. 100504–100504. 3 indexed citations

Wang, Yafei, John Metzcar, Elmar Bucher, et al.. (2025). Drug-loaded nanoparticles for cancer therapy: A high-throughput multicellular agent-based modeling study. Journal of Theoretical Biology. 616. 112266–112266.

Li, Wenhui & Vikram Jadhao. (2024). Comparing Phenomenological Models of Shear Thinning of Alkanes at Low and High Newtonian Viscosities. Tribology Letters. 72(4). 1 indexed citations

Jadhao, Vikram, et al.. (2023). Molecular Dynamics Simulations of Deformable Viral Capsomers. Viruses. 15(8). 1672–1672. 1 indexed citations

Jadhao, Vikram, et al.. (2023). Shape control of deformable charge-patterned nanoparticles. Physical review. E. 107(1). 14502–14502. 1 indexed citations

Fox, Geoffrey, et al.. (2022). Solving Newton’s equations of motion with large timesteps using recurrent neural networks based operators. Machine Learning Science and Technology. 3(2). 25002–25002. 15 indexed citations

Uchida, Masaki, et al.. (2022). Multilayered Ordered Protein Arrays Self-Assembled from a Mixed Population of Virus-like Particles. ACS Nano. 16(5). 7662–7673. 10 indexed citations

10.

Jadhao, Vikram, et al.. (2020). Designing Surface Charge Patterns for Shape Control of Deformable Nanoparticles. Physical Review Letters. 125(24). 6 indexed citations

11.

Jadhao, Vikram, et al.. (2020). A Novel Coronavirus (nCOV- 2019): A Pandemic Severe Respiratory Tract Infections by SARS COV-2 in Human. Journal of Drug Delivery and Therapeutics. 10(3-s). 271–279.

12.

Uchida, Masaki, Byeongdu Lee, Masafumi Fukuto, et al.. (2019). Linker-Mediated Assembly of Virus-Like Particles into Ordered Arrays via Electrostatic Control. ACS Applied Bio Materials. 2(5). 2192–2201. 21 indexed citations

13.

Jadhao, Vikram, et al.. (2019). Computational studies of shape control of charged deformable nanocontainers. Journal of Materials Chemistry B. 7(41). 6370–6382. 7 indexed citations

14.

Fox, Geoffrey, et al.. (2019). Machine Learning for Auto-tuning of Simulation Parameters in Car-Parrinello Molecular Dynamics. Bulletin of the American Physical Society. 2019. 1 indexed citations

15.

Jadhao, Vikram & Mark O. Robbins. (2017). Probing large viscosities in glass-formers with nonequilibrium simulations. Proceedings of the National Academy of Sciences. 114(30). 7952–7957. 67 indexed citations

16.

Jadhao, Vikram, et al.. (2015). Coulomb energy of uniformly charged spheroidal shell systems. Physical Review E. 91(3). 32305–32305. 17 indexed citations

17.

Jadhao, Vikram, Francisco J. Solis, & Mónica Olvera de la Cruz. (2013). Free-energy functionals of the electrostatic potential for Poisson-Boltzmann theory. Physical Review E. 88(2). 22305–22305. 8 indexed citations

18.

Solis, Francisco J., Vikram Jadhao, & Mónica Olvera de la Cruz. (2013). Generating true minima in constrained variational formulations via modified Lagrange multipliers. Physical Review E. 88(5). 53306–53306. 6 indexed citations

19.

Jadhao, Vikram, Francisco J. Solis, & Mónica Olvera de la Cruz. (2013). A variational formulation of electrostatics in a medium with spatially varying dielectric permittivity. The Journal of Chemical Physics. 138(5). 54119–54119. 34 indexed citations

20.

Jadhao, Vikram, Francisco J. Solis, & Mónica Olvera de la Cruz. (2012). Simulation of Charged Systems in Heterogeneous Dielectric Media via a True Energy Functional. Physical Review Letters. 109(22). 223905–223905. 61 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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