R. Narulkar

405 total citations
10 papers, 342 citations indexed

About

R. Narulkar is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics and Mechanical Engineering. According to data from OpenAlex, R. Narulkar has authored 10 papers receiving a total of 342 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Materials Chemistry, 5 papers in Atomic and Molecular Physics, and Optics and 5 papers in Mechanical Engineering. Recurrent topics in R. Narulkar's work include Machine Learning in Materials Science (4 papers), Diamond and Carbon-based Materials Research (4 papers) and Metal and Thin Film Mechanics (4 papers). R. Narulkar is often cited by papers focused on Machine Learning in Materials Science (4 papers), Diamond and Carbon-based Materials Research (4 papers) and Metal and Thin Film Mechanics (4 papers). R. Narulkar collaborates with scholars based in United States. R. Narulkar's co-authors include R. Komanduri, Lionel M. Raff, Satish Bukkapatnam, M. Malshe, Martin Hagan and Paras M. Agrawal and has published in prestigious journals such as The Journal of Chemical Physics, The Journal of Physical Chemistry A and Tribology International.

In The Last Decade

R. Narulkar

10 papers receiving 334 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
R. Narulkar United States	8	245	129	89	87	46	10	342
Mikołaj Kowalik Poland	11	184 0.8×	23 0.2×	151 1.7×	86 1.0×	90 2.0×	17	397
Siamak Dadras United States	14	247 1.0×	128 1.0×	84 0.9×	102 1.2×	12 0.3×	24	547
Lucian Anton Romania	10	98 0.4×	44 0.3×	30 0.3×	49 0.6×	8 0.2×	17	258
S. Lee United States	7	98 0.4×	373 2.9×	23 0.3×	65 0.7×	8 0.2×	10	562
Hugues Meyer Germany	10	74 0.3×	73 0.6×	61 0.7×	42 0.5×	11 0.2×	18	314
Song Feng China	13	163 0.7×	33 0.3×	18 0.2×	46 0.5×	17 0.4×	50	539
Yaolong Li China	11	149 0.6×	68 0.5×	5 0.1×	95 1.1×	20 0.4×	47	373
Wenlai Huang China	10	103 0.4×	21 0.2×	24 0.3×	64 0.7×	7 0.2×	13	327

Countries citing papers authored by R. Narulkar

Since Specialization

Citations

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

Fields of papers citing papers by R. Narulkar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of R. Narulkar

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

All Works

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10 of 10 papers shown

Agrawal, Paras M., R. Narulkar, Satish Bukkapatnam, Lionel M. Raff, & R. Komanduri. (2009). A phenomenological model of polishing of silicon with diamond abrasive. Tribology International. 43(1-2). 100–107. 13 indexed citations

Malshe, M., R. Narulkar, Lionel M. Raff, et al.. (2009). Development of generalized potential-energy surfaces using many-body expansions, neural networks, and moiety energy approximations. The Journal of Chemical Physics. 130(18). 184102–184102. 52 indexed citations

Agrawal, Paras M., M. Malshe, R. Narulkar, et al.. (2009). A Self-Starting Method for Obtaining Analytic Potential-Energy Surfaces from ab Initio Electronic Structure Calculations. The Journal of Physical Chemistry A. 113(5). 869–877. 12 indexed citations

Narulkar, R.. (2009). Investigation on the mechanism of wear of single crystal diamond tool in nanometric cutting of iron using molecular dynamics (MD) and the development of generalized potential energy surfaces (GPES) based on ab initio calculations. 7 indexed citations

Malshe, M., et al.. (2009). Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks. The Journal of Chemical Physics. 130(13). 134101–134101. 102 indexed citations

Narulkar, R., Satish Bukkapatnam, Lionel M. Raff, & R. Komanduri. (2008). Molecular dynamics simulations of diffusion of carbon into iron. The Philosophical Magazine A Journal of Theoretical Experimental and Applied Physics. 88(8). 1259–1275. 22 indexed citations

Narulkar, R., Satish Bukkapatnam, Lionel M. Raff, & R. Komanduri. (2008). Graphitization as a precursor to wear of diamond in machining pure iron: A molecular dynamics investigation. Computational Materials Science. 45(2). 358–366. 88 indexed citations

Malshe, M., R. Narulkar, Lionel M. Raff, et al.. (2008). Parametrization of analytic interatomic potential functions using neural networks. The Journal of Chemical Physics. 129(4). 44111–44111. 36 indexed citations

Komanduri, R., R. Narulkar, & Lionel M. Raff. (2004). Monte Carlo simulation of nanometric cutting. The Philosophical Magazine A Journal of Theoretical Experimental and Applied Physics. 84(11). 1155–1183. 8 indexed citations

10.

Narulkar, R., Lionel M. Raff, & R. Komanduri. (2004). Monte Carlo-steepest descent (MC-SD) simulations of nanometric cutting. 218(1). 7–16. 2 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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