Marco Ragone

766 total citations
19 papers, 603 citations indexed

About

Marco Ragone is a scholar working on Electrical and Electronic Engineering, Automotive Engineering and Materials Chemistry. According to data from OpenAlex, Marco Ragone has authored 19 papers receiving a total of 603 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Electrical and Electronic Engineering, 12 papers in Automotive Engineering and 5 papers in Materials Chemistry. Recurrent topics in Marco Ragone's work include Advancements in Battery Materials (13 papers), Advanced Battery Technologies Research (12 papers) and Advanced Battery Materials and Technologies (6 papers). Marco Ragone is often cited by papers focused on Advancements in Battery Materials (13 papers), Advanced Battery Technologies Research (12 papers) and Advanced Battery Materials and Technologies (6 papers). Marco Ragone collaborates with scholars based in United States and Germany. Marco Ragone's co-authors include Farzad Mashayek, Vitaliy Yurkiv, Ajaykrishna Ramasubramanian, Reza Shahbazian‐Yassar, Tara Foroozan, Babak Kashir, Vahid Jabbari, Boao Song, Lance Long and Mahmoud Tamadoni Saray and has published in prestigious journals such as Journal of The Electrochemical Society, Journal of Power Sources and The Journal of Physical Chemistry C.

In The Last Decade

Marco Ragone

18 papers receiving 587 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marco Ragone United States 9 495 378 71 41 31 19 603
Ajaykrishna Ramasubramanian United States 9 661 1.3× 492 1.3× 83 1.2× 42 1.0× 32 1.0× 18 767
Peter J. Weddle United States 16 690 1.4× 559 1.5× 69 1.0× 94 2.3× 42 1.4× 56 833
Supratim Das United States 11 902 1.8× 754 2.0× 69 1.0× 70 1.7× 21 0.7× 19 1.0k
Julian Feinauer Germany 12 339 0.7× 269 0.7× 58 0.8× 71 1.7× 16 0.5× 14 441
Petr Vyroubal Czechia 9 249 0.5× 185 0.5× 34 0.5× 29 0.7× 20 0.6× 42 328
Shengqiang Li China 7 616 1.2× 252 0.7× 125 1.8× 67 1.6× 24 0.8× 18 701
Karsten Richter Germany 11 623 1.3× 583 1.5× 40 0.6× 58 1.4× 7 0.2× 15 704
Guoqing Ma China 6 253 0.5× 168 0.4× 83 1.2× 23 0.6× 45 1.5× 11 385
Hailong Zhang China 15 440 0.9× 191 0.5× 36 0.5× 58 1.4× 56 1.8× 54 537

Countries citing papers authored by Marco Ragone

Since Specialization
Citations

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

Fields of papers citing papers by Marco Ragone

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marco Ragone

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

All Works

19 of 19 papers shown
1.
Ragone, Marco, et al.. (2024). A combined multiphysics modeling and deep learning framework to predict thermal runaway in cylindrical Li-ion batteries. Journal of Power Sources. 595. 234065–234065. 28 indexed citations
2.
Ragone, Marco, Abhijit H. Phakatkar, Lance Long, et al.. (2024). Predicting column heights and elemental composition in experimental transmission electron microscopy images of high-entropy oxides using deep learning. npj Computational Materials. 10(1). 4 indexed citations
3.
Ragone, Marco, et al.. (2023). Deep learning modeling in microscopy imaging: A review of materials science applications. Progress in Materials Science. 138. 101165–101165. 25 indexed citations
4.
Ragone, Marco, et al.. (2022). A Combined Multi-Physics Modelling and Machine Learning to Predict Electro-Thermal Failures of Cylindrical Li-Ion Batteries. ECS Meeting Abstracts. MA2022-01(2). 190–190. 3 indexed citations
5.
Yurkiv, Vitaliy, et al.. (2022). Revealing the Structure and Properties of Polycrystalline Components of the Solid Electrolyte Interface. ECS Meeting Abstracts. MA2022-01(2). 251–251. 3 indexed citations
6.
Ragone, Marco, et al.. (2022). Predicting Thermal Failures Using an Advanced Data-Driven Modeling Framework in a Cylindrical Li-Ion Battery Pack. ECS Meeting Abstracts. MA2022-02(3). 230–230. 3 indexed citations
7.
Ragone, Marco, et al.. (2022). Advanced Data-Driven Modeling Framework for Predicting Thermal Failures in Li-Ion Pouch Batteries. ECS Meeting Abstracts. MA2022-01(2). 434–434. 3 indexed citations
8.
9.
Kashir, Babak, Marco Ragone, Ajaykrishna Ramasubramanian, Vitaliy Yurkiv, & Farzad Mashayek. (2021). Application of fully convolutional neural networks for feature extraction in fluid flow. Journal of Visualization. 24(4). 771–785. 14 indexed citations
10.
Ragone, Marco, Mahmoud Tamadoni Saray, Lance Long, et al.. (2021). Deep learning for mapping element distribution of high-entropy alloys in scanning transmission electron microscopy images. Computational Materials Science. 201. 110905–110905. 17 indexed citations
11.
Ragone, Marco, et al.. (2020). Atomic column heights detection in metallic nanoparticles using deep convolutional learning. Computational Materials Science. 180. 109722–109722. 23 indexed citations
12.
Ragone, Marco, Vitaliy Yurkiv, Ajaykrishna Ramasubramanian, et al.. (2020). Data Driven Approach for Predicting Thermal Runaway in Li Ion Battery. ECS Meeting Abstracts. MA2020-02(4). 818–818.
13.
Yurkiv, Vitaliy, Tara Foroozan, Ajaykrishna Ramasubramanian, et al.. (2020). Understanding Zn Electrodeposits Morphology in Secondary Batteries Using Phase-Field Model. Journal of The Electrochemical Society. 167(6). 60503–60503. 36 indexed citations
14.
Ramasubramanian, Ajaykrishna, Vitaliy Yurkiv, Tara Foroozan, et al.. (2020). Stability of Solid-Electrolyte Interphase (SEI) on the Lithium Metal Surface in Lithium Metal Batteries (LMBs). ACS Applied Energy Materials. 3(11). 10560–10567. 61 indexed citations
15.
Ragone, Marco, Vitaliy Yurkiv, Ajaykrishna Ramasubramanian, Reza Shahbazian‐Yassar, & Farzad Mashayek. (2020). Predicting Thermal Runaway in Li-Ion Battery Employing Machine Learning Framework. ECS Meeting Abstracts. MA2020-01(2). 429–429. 1 indexed citations
16.
Yurkiv, Vitaliy, Tara Foroozan, Ajaykrishna Ramasubramanian, et al.. (2020). The Mechanism of Zn Diffusion Through ZnO in Secondary Battery: A Combined Theoretical and Experimental Study. The Journal of Physical Chemistry C. 124(29). 15730–15738. 3 indexed citations
17.
Ragone, Marco, Vitaliy Yurkiv, Ajaykrishna Ramasubramanian, Babak Kashir, & Farzad Mashayek. (2020). Data driven estimation of electric vehicle battery state-of-charge informed by automotive simulations and multi-physics modeling. Journal of Power Sources. 483. 229108–229108. 108 indexed citations
18.
Ramasubramanian, Ajaykrishna, Vitaliy Yurkiv, Tara Foroozan, et al.. (2019). Lithium Diffusion Mechanism through Solid–Electrolyte Interphase in Rechargeable Lithium Batteries. The Journal of Physical Chemistry C. 123(16). 10237–10245. 269 indexed citations
19.
Kashir, Babak, Marco Ragone, Vitaliy Yurkiv, & Farzad Mashayek. (2019). Data-driven prediction of vortical structures in turbulent flows employing deep learning techniques. Bulletin of the American Physical Society. 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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