Ghatu Subhash

8.7k total citations
210 papers, 7.1k citations indexed

About

Ghatu Subhash is a scholar working on Materials Chemistry, Mechanics of Materials and Mechanical Engineering. According to data from OpenAlex, Ghatu Subhash has authored 210 papers receiving a total of 7.1k indexed citations (citations by other indexed papers that have themselves been cited), including 113 papers in Materials Chemistry, 99 papers in Mechanics of Materials and 90 papers in Mechanical Engineering. Recurrent topics in Ghatu Subhash's work include Advanced ceramic materials synthesis (50 papers), Metal and Thin Film Mechanics (49 papers) and High-Velocity Impact and Material Behavior (45 papers). Ghatu Subhash is often cited by papers focused on Advanced ceramic materials synthesis (50 papers), Metal and Thin Film Mechanics (49 papers) and High-Velocity Impact and Material Behavior (45 papers). Ghatu Subhash collaborates with scholars based in United States, Switzerland and India. Ghatu Subhash's co-authors include X.-L. Gao, Dipankar Ghosh, Nagaraj K. Arakere, Qunli Liu, Robert J. Dowding, Spandan Maiti, Laszlo J. Kecskes, J.S. Tulenko, Michael A. Klecka and Douglas E. Spearot and has published in prestigious journals such as Applied Physics Letters, PLoS ONE and Journal of Applied Physics.

In The Last Decade

Ghatu Subhash

208 papers receiving 6.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ghatu Subhash United States 47 3.6k 3.5k 2.8k 1.5k 1.5k 210 7.1k
Akira Kawasaki Japan 45 4.5k 1.3× 3.3k 0.9× 1.7k 0.6× 729 0.5× 2.7k 1.8× 319 7.7k
Mark Hoffman Australia 46 1.9k 0.5× 4.0k 1.1× 2.6k 0.9× 2.0k 1.3× 890 0.6× 233 6.8k
Chunsheng Lu China 43 1.9k 0.5× 2.5k 0.7× 1.9k 0.7× 693 0.5× 955 0.7× 244 5.7k
Andreas Mortensen Switzerland 51 7.2k 2.0× 3.8k 1.1× 3.1k 1.1× 784 0.5× 2.6k 1.8× 261 10.3k
Matthew R. Begley United States 41 1.6k 0.5× 2.2k 0.6× 1.7k 0.6× 1.7k 1.1× 1.2k 0.8× 172 6.1k
M.D. Thouless United States 49 2.4k 0.7× 1.8k 0.5× 4.7k 1.7× 2.1k 1.4× 791 0.5× 168 9.3k
F.D. Fischer Austria 55 6.3k 1.8× 5.8k 1.6× 3.8k 1.3× 1.3k 0.8× 389 0.3× 329 10.7k
Gilbert Fantozzi France 46 3.3k 0.9× 3.6k 1.0× 1.3k 0.5× 1.5k 1.0× 3.8k 2.6× 329 7.9k
Sheldon M. Wiederhorn United States 45 3.2k 0.9× 2.9k 0.8× 2.5k 0.9× 1.0k 0.7× 4.1k 2.8× 147 8.1k
Chun‐Hway Hsueh Taiwan 39 2.5k 0.7× 1.6k 0.4× 1.8k 0.6× 1.2k 0.8× 1.1k 0.7× 222 5.8k

Countries citing papers authored by Ghatu Subhash

Since Specialization
Citations

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

Fields of papers citing papers by Ghatu Subhash

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ghatu Subhash

This figure shows the co-authorship network connecting the top 25 collaborators of Ghatu Subhash. A scholar is included among the top collaborators of Ghatu Subhash 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 Ghatu Subhash. Ghatu Subhash 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.
Subhash, Ghatu, et al.. (2025). Machine learning for computational fracture and damage mechanics— Status and perspectives. Engineering Fracture Mechanics. 332. 111778–111778.
2.
Kolinski, John M., et al.. (2024). Fluid-mediated impact of soft solids. Journal of Fluid Mechanics. 997. 4 indexed citations
3.
Spearot, Douglas E., et al.. (2024). A Genetic Algorithm Trained Machine-Learned Interatomic Potential for the Silicon–Carbon System. The Journal of Physical Chemistry C. 128(29). 12213–12226. 3 indexed citations
4.
Hennig, Richard G., et al.. (2023). Augmenting the discovery of computationally complex ceramics for extreme environments with machine learning. Journal of materials research/Pratt's guide to venture capital sources. 38(23). 5055–5064. 2 indexed citations
5.
Subhash, Ghatu, et al.. (2023). Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis. Chemistry of Materials. 35(3). 1186–1200. 6 indexed citations
6.
Subhash, Ghatu, et al.. (2023). Concept Article: A Novel Compact Millipede Bar Waveguide for Propagation of Longitudinal Stress Waves. Journal of Dynamic Behavior of Materials. 12(1). 26–36. 1 indexed citations
7.
Subhash, Ghatu, et al.. (2022). Stress wave propagation through a 180° bend junction in a square cross-sectional bar. International Journal of Engineering Science. 180. 103748–103748. 2 indexed citations
8.
Subhash, Ghatu, et al.. (2022). Influence of weave architecture on mechanical response of SiCf-SiCm tubular composites. Materials Today Communications. 33. 104206–104206. 7 indexed citations
9.
Subhash, Ghatu, et al.. (2021). Measurement of Residual Stress in Silicon Carbide Fibers of Tubular Composites Using Raman Spectroscopy. Acta Materialia. 217. 117164–117164. 61 indexed citations
10.
Luo, Ke, Ghatu Subhash, & Douglas E. Spearot. (2021). On shockwave propagation and attenuation in poly(ethylene glycol) diacrylate hydrogels. Journal of the mechanical behavior of biomedical materials. 118. 104423–104423. 5 indexed citations
11.
Zhou, Hui, et al.. (2021). Label-free quantification of soft tissue alignment by polarized Raman spectroscopy. Acta Biomaterialia. 136. 363–374. 6 indexed citations
12.
Rudawski, Nicholas G., et al.. (2019). Oxidation of the polycrystalline copper–graphene nanocomposite. Journal of Physics Materials. 2(2). 25005–25005. 6 indexed citations
13.
Subhash, Ghatu, et al.. (2018). Comparison of pressure-sensitive strength models for ceramics under ultrahigh confinement. International Journal of Impact Engineering. 118. 60–66. 24 indexed citations
14.
Tulenko, J.S., et al.. (2017). Exploration of Viability of Spark Plasma Sintering for Commercial Fabrication of Nuclear Fuel Pellets. Nuclear Technology. 200(2). 144–158. 8 indexed citations
15.
Kelly, Karen R., et al.. (2017). Controlled single bubble cavitation collapse results in jet-induced injury in brain tissue. Journal of the mechanical behavior of biomedical materials. 74. 261–273. 24 indexed citations
16.
Sarntinoranont, Malisa, et al.. (2017). Simulated blast overpressure induces specific astrocyte injury in an ex vivo brain slice model. PLoS ONE. 12(4). e0175396–e0175396. 14 indexed citations
17.
Subhash, Ghatu, et al.. (2017). Wave propagation in ballistic gelatine. Journal of the mechanical behavior of biomedical materials. 68. 32–41. 24 indexed citations
18.
Liu, Qunli, Ghatu Subhash, & David F. Moore. (2011). Loading velocity dependent permeability in agarose gel under compression. Journal of the mechanical behavior of biomedical materials. 4(7). 974–982. 13 indexed citations
19.
Gao, X.-L., et al.. (2005). Two new expanding cavity models for indentation deformations of elastic strain-hardening materials. International Journal of Solids and Structures. 43(7-8). 2193–2208. 157 indexed citations
20.
Subhash, Ghatu. (1995). The constitutive behavior of refractory metals as a function of strain rate. JOM. 47(5). 55–58. 21 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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