Badri Narayanan

6.8k total citations · 5 hit papers
115 papers, 5.3k citations indexed

About

Badri Narayanan is a scholar working on Materials Chemistry, Electrical and Electronic Engineering and Mechanical Engineering. According to data from OpenAlex, Badri Narayanan has authored 115 papers receiving a total of 5.3k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Materials Chemistry, 41 papers in Electrical and Electronic Engineering and 24 papers in Mechanical Engineering. Recurrent topics in Badri Narayanan's work include Machine Learning in Materials Science (20 papers), Advancements in Battery Materials (19 papers) and Advanced Battery Materials and Technologies (17 papers). Badri Narayanan is often cited by papers focused on Machine Learning in Materials Science (20 papers), Advancements in Battery Materials (19 papers) and Advanced Battery Materials and Technologies (17 papers). Badri Narayanan collaborates with scholars based in United States, Singapore and India. Badri Narayanan's co-authors include Subramanian K. R. S. Sankaranarayanan, Larry A. Curtiss, Mathew J. Cherukara, Henry Chan, Ganesh Kamath, Ali Erdemir, Chun Yuen Kwok, Quanquan Pang, Linda F. Nazar and Troy D. Loeffler and has published in prestigious journals such as Nature, Science and Journal of the American Chemical Society.

In The Last Decade

Badri Narayanan

111 papers receiving 5.2k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Tuning the electrolyte network structure to invoke quasi-solid state sulfur conversion and suppress lithium dendrite formation in Li–S batteries

2018 507 citations Badri Narayanan, Larry A. Curtiss et al. profile →
A lithium–oxygen battery with a long cycle life in an air-like atmosphere

2018 496 citations Mohammad Asadi, Baharak Sayahpour et al. profile →
Carbon-based tribofilms from lubricating oils

2016 450 citations Badri Narayanan, Ganesh Kamath et al. profile →
Machine learning enabled autonomous microstructural characterization in 3D samples

2020 324 citations Henry Chan, Mathew J. Cherukara et al. profile →
Fast-charging aluminium–chalcogen batteries resistant to dendritic shorting

2022 170 citations Badri Narayanan et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Badri Narayanan United States	34	2.5k	2.4k	1.1k	675	655	115	5.3k
Katsuyo Thornton United States	40	2.9k 1.1×	3.8k 1.6×	1.2k 1.0×	1.9k 2.8×	324 0.5×	159	7.4k
Jin‐Cheng Zheng China	49	5.6k 2.2×	3.3k 1.4×	489 0.4×	293 0.4×	303 0.5×	215	8.1k
Bohayra Mortazavi Germany	55	7.5k 2.9×	2.4k 1.0×	522 0.5×	236 0.3×	648 1.0×	154	8.8k
Chen Shen China	49	3.6k 1.4×	2.1k 0.9×	2.3k 2.0×	184 0.3×	512 0.8×	304	7.3k
James E. Saal United States	35	5.3k 2.1×	1.3k 0.5×	2.4k 2.1×	162 0.2×	408 0.6×	76	7.4k
Kee‐Sun Sohn South Korea	48	4.7k 1.8×	3.6k 1.5×	528 0.5×	408 0.6×	183 0.3×	221	6.8k
Christopher Wolverton United States	44	6.8k 2.7×	5.6k 2.3×	1.1k 1.0×	1.2k 1.8×	179 0.3×	137	10.5k
Taylor D. Sparks United States	31	3.0k 1.2×	1.1k 0.5×	512 0.5×	196 0.3×	164 0.3×	112	4.0k
Lan Zhou China	41	3.4k 1.3×	2.0k 0.8×	1.0k 0.9×	293 0.4×	342 0.5×	301	7.6k
Monika M. Biener United States	36	3.9k 1.5×	1.2k 0.5×	1.6k 1.4×	543 0.8×	511 0.8×	102	7.0k

Countries citing papers authored by Badri Narayanan

Since Specialization

Citations

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

Fields of papers citing papers by Badri Narayanan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Badri Narayanan

This figure shows the co-authorship network connecting the top 25 collaborators of Badri Narayanan. A scholar is included among the top collaborators of Badri Narayanan 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 Badri Narayanan. Badri Narayanan 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

Guo, Xiaolin, Yan Chen, Dunji Yu, et al.. (2025). Synthesis and post-heating treatment of inorganic NaF·Na3SbS4 solid electrolytes. Nano Energy. 137. 110770–110770.

Yang, Li, William Arnold, Xiaolin Guo, et al.. (2023). Solvent-free and low temperature synthesis of chalcogenide Na superionic conductors for solid-state batteries. Chemical Engineering Journal. 468. 143624–143624. 8 indexed citations

Dwivedi, Neeraj, Kiran Sasikumar, Chunmeng Dou, et al.. (2021). Graphene overcoats for ultra-high storage density magnetic media. National University of Singapore. 43 indexed citations

Zhang, Yuan, Daniel J. Trainer, Badri Narayanan, et al.. (2021). One-Dimensional Lateral Force Anisotropy at the Atomic Scale in Sliding Single Molecules on a Surface. Nano Letters. 21(15). 6391–6397. 4 indexed citations

Kempaiah, Ravindra, Henry Chan, Srilok Srinivasan, et al.. (2021). Impact of Stabilizing Cations on Lithium Intercalation in Tunneled Manganese Oxide Cathodes. ACS Applied Energy Materials. 4(11). 12099–12111. 10 indexed citations

Sasikumar, Kiran, Henry Chan, Badri Narayanan, & Subramanian K. R. S. Sankaranarayanan. (2019). Machine Learning Applied to a Variable Charge Atomistic Model for Cu/Hf Binary Alloy Oxide Heterostructures. Chemistry of Materials. 31(9). 3089–3102. 19 indexed citations

Patra, Tarak K., Henry Chan, Paul Podsiadlo, et al.. (2019). Ligand dynamics control structure, elasticity, and high-pressure behavior of nanoparticle superlattices. Nanoscale. 11(22). 10655–10666. 21 indexed citations

Chan, Henry, Kiran Sasikumar, Srilok Srinivasan, et al.. (2019). Machine learning a bond order potential model to study thermal transport in WSe₂nanostructures. Nanoscale. 11(21). 10381–10392. 28 indexed citations

Chan, Henry, Badri Narayanan, Mathew J. Cherukara, et al.. (2019). Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data. The Journal of Physical Chemistry C. 123(12). 6941–6957. 80 indexed citations

10.

Ahmadiparidari, Alireza, Robert E. Warburton, L. Majidi, et al.. (2019). A Long‐Cycle‐Life Lithium–CO₂ Battery with Carbon Neutrality. Advanced Materials. 31(40). e1902518–e1902518. 181 indexed citations

11.

Loeffler, Troy D., Henry Chan, Kiran Sasikumar, et al.. (2019). Teaching an Old Dog New Tricks: Machine Learning an Improved TIP3P Potential Model for Liquid–Vapor Phase Phenomena. The Journal of Physical Chemistry C. 123(36). 22643–22655. 11 indexed citations

12.

Patra, Tarak K., Fu Zhang, Daniel S. Schulman, et al.. (2018). Defect Dynamics in 2-D MoS₂ Probed by Using Machine Learning, Atomistic Simulations, and High-Resolution Microscopy. ACS Nano. 12(8). 8006–8016. 87 indexed citations

13.

Yang, Hao‐Cheng, Yunsong Xie, Henry Chan, et al.. (2018). Crude-Oil-Repellent Membranes by Atomic Layer Deposition: Oxide Interface Engineering. ACS Nano. 12(8). 8678–8685. 162 indexed citations

14.

Wang, Yifan, Henry Chan, Badri Narayanan, et al.. (2017). Thermomechanical Response of Self-Assembled Nanoparticle Membranes. ACS Nano. 11(8). 8026–8033. 14 indexed citations

15.

Narayanan, Badri, Henry Chan, Alper Kınacı, et al.. (2017). Machine learnt bond order potential to model metal–organic (Co–C) heterostructures. Nanoscale. 9(46). 18229–18239. 13 indexed citations

16.

Sun, Yugang, Xiaobing Zuo, Subramanian K. R. S. Sankaranarayanan, et al.. (2017). Quantitative 3D evolution of colloidal nanoparticle oxidation in solution. Science. 356(6335). 303–307. 131 indexed citations

17.

Cherukara, Mathew J., Badri Narayanan, Henry Chan, & Subramanian K. R. S. Sankaranarayanan. (2017). Silicene growth through island migration and coalescence. Nanoscale. 9(29). 10186–10192. 17 indexed citations

18.

Sasikumar, Kiran, Badri Narayanan, Mathew J. Cherukara, et al.. (2017). Evolutionary Optimization of a Charge Transfer Ionic Potential Model for Ta/Ta-Oxide Heterointerfaces. Chemistry of Materials. 29(8). 3603–3614. 24 indexed citations

19.

Cherukara, Mathew J., Kiran Sasikumar, Wonsuk Cha, et al.. (2016). Ultrafast Three-Dimensional X-ray Imaging of Deformation Modes in ZnO Nanocrystals. Nano Letters. 17(2). 1102–1108. 18 indexed citations

20.

Zapol, Peter, Dmitry Karpeyev, Ketan Maheshwari, et al.. (2015). Coupled molecular-dynamics and first-principle transport calculations of metal/oxide/metal heterostructures. Bulletin of the American Physical Society. 2015. 1 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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