M. Malshe

599 total citations
11 papers, 497 citations indexed

About

M. Malshe is a scholar working on Atomic and Molecular Physics, and Optics, Materials Chemistry and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, M. Malshe has authored 11 papers receiving a total of 497 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Atomic and Molecular Physics, and Optics, 8 papers in Materials Chemistry and 2 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in M. Malshe's work include Machine Learning in Materials Science (8 papers), Advanced Chemical Physics Studies (8 papers) and Spectroscopy and Quantum Chemical Studies (4 papers). M. Malshe is often cited by papers focused on Machine Learning in Materials Science (8 papers), Advanced Chemical Physics Studies (8 papers) and Spectroscopy and Quantum Chemical Studies (4 papers). M. Malshe collaborates with scholars based in United States. M. Malshe's co-authors include R. Komanduri, Lionel M. Raff, Martin Hagan, Satish Bukkapatnam, Mark G. Rockley, R. Narulkar, Paras M. Agrawal, Bruce Benjamin, Hui Yang and Drew Dawson∥ and has published in prestigious journals such as The Journal of Chemical Physics, Physical Review B and The Journal of Physical Chemistry A.

In The Last Decade

M. Malshe

11 papers receiving 487 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M. Malshe United States 10 299 227 103 68 67 11 497
Andreas Mardt Germany 5 149 0.5× 45 0.2× 77 0.7× 3 0.0× 219 3.3× 6 374
Karthik Siva United States 9 108 0.4× 183 0.8× 66 0.6× 46 0.7× 12 339
Junta Doi Japan 11 118 0.4× 72 0.3× 26 0.3× 4 0.1× 234 3.5× 37 394
Ryan Pederson United States 9 183 0.6× 109 0.5× 68 0.7× 43 0.6× 13 298
Junjie Yang United States 8 121 0.4× 153 0.7× 42 0.4× 63 0.9× 18 313
Claire P. Massen United Kingdom 6 137 0.5× 74 0.3× 42 0.4× 46 0.7× 7 390
Wei-Chen Chen Taiwan 14 53 0.2× 61 0.3× 38 0.4× 30 0.4× 57 600
Makoto Ohya Japan 9 55 0.2× 278 1.2× 25 0.2× 2 0.0× 28 0.4× 32 468
Joshua T. Horton United Kingdom 9 266 0.9× 80 0.4× 172 1.7× 191 2.9× 15 451
Dmitry Solenov United States 14 116 0.4× 384 1.7× 48 0.5× 3 0.0× 45 0.7× 41 670

Countries citing papers authored by M. Malshe

Since Specialization
Citations

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

Fields of papers citing papers by M. Malshe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Malshe

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

All Works

11 of 11 papers shown
1.
Malshe, M., Lionel M. Raff, Martin Hagan, Satish Bukkapatnam, & R. Komanduri. (2010). Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databases. The Journal of Chemical Physics. 132(20). 204103–204103. 6 indexed citations
2.
3.
Dawson∥, Drew, Hui Yang, M. Malshe, et al.. (2009). Linear affine transformations between 3-lead (Frank XYZ leads) vectorcardiogram and 12-lead electrocardiogram signals. Journal of Electrocardiology. 42(6). 622–630. 58 indexed citations
4.
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
5.
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
6.
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
7.
Bukkapatnam, Satish, R. Komanduri, Hui Yang, et al.. (2008). Classification of atrial fibrillation episodes from sparse electrocardiogram data. Journal of Electrocardiology. 41(4). 292–299. 18 indexed citations
8.
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
9.
10.
Bukkapatnam, Satish, M. Malshe, Paras M. Agrawal, Lionel M. Raff, & R. Komanduri. (2006). Parametrization of interatomic potential functions using a genetic algorithm accelerated with a neural network. Physical Review B. 74(22). 19 indexed citations
11.
Raff, Lionel M., et al.. (2005). Ab initio potential-energy surfaces for complex, multichannel systems using modified novelty sampling and feedforward neural networks. The Journal of Chemical Physics. 122(8). 84104–84104. 132 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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