Chi Chen
Impact in
-
- Electrocatalysts for Energy Conversion
- Materials Chemistry top 0.2%
- Machine Learning in Materials Science
- Advancements in Solid Oxide Fuel Cells
- Electronic and Structural Properties of Oxides
Papers in
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- Electrocatalysts for Energy Conversion 37
-
- Machine Learning in Materials Science 25
- Co-authors
- Francesco Ciucci (26 shared papers)Shyue Ping Ong (32 shared papers)Mattia Saccoccio (7 shared papers)Ting Hei Wan (4 shared papers)Yunxing Zuo (8 shared papers)Weike Ye (6 shared papers)Dengjie Chen (14 shared papers)Xiangguo Li (9 shared papers)
- Journals
- npj Computational Materials (6 papers)Journal of Materials Chemistry A (6 papers)Chemistry of Materials (6 papers)Thin Solid Films (5 papers)Electrochimica Acta (5 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Chi Chen
223 papers receiving 17.1k citations
Chi Chen's Hit Papers
Peers
Comparison fields: 5 of 160
- Renewable Energy, Sustainability and the Environment 5.9k
- Materials Chemistry 8.5k
- Catalysis 1.0k
- Electrical and Electronic Engineering 8.4k
- Electrochemistry 660
Countries citing papers authored by Chi Chen
This map shows the geographic impact of Chi Chen'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 Chi Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chi Chen more than expected).
Fields of papers citing papers by Chi Chen
This network shows the impact of papers produced by Chi Chen. 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 Chi Chen. The network helps show where Chi Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Chi Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 234 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Influence of the Discretization Methods on the Distribution of Relaxation Times Deconvolution: Implementing Radial Basis Functions with DRTtools Hit paper breakdown → | 2015 | 1943 |
| 2 | Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals Hit paper breakdown → | 2019 | 1053 |
| 3 | Nonstoichiometric Oxides as Low-Cost and Highly-Efficient Oxygen Reduction/Evolution Catalysts for Low-Temperature Electrochemical Devices Hit paper breakdown → | 2015 | 862 |
| 4 | Recent advances and applications of deep learning methods in materials science Hit paper breakdown → | 2022 | 787 |
| 5 | Performance and Cost Assessment of Machine Learning Interatomic Potentials Hit paper breakdown → | 2020 | 695 |
| 6 | A universal graph deep learning interatomic potential for the periodic table Hit paper breakdown → | 2022 | 685 |
| 7 | Progress toward Commercial Application of Electrochemical Carbon Dioxide Reduction Hit paper breakdown → | 2018 | 640 |
| 8 | Phenylenediamine-Based FeNx/C Catalyst with High Activity for Oxygen Reduction in Acid Medium and Its Active-Site Probing Hit paper breakdown → | 2014 | 611 |
| 9 | S‐Doping of an Fe/N/C ORR Catalyst for Polymer Electrolyte Membrane Fuel Cells with High Power Density Hit paper breakdown → | 2015 | 587 |
| 10 | Analysis of Electrochemical Impedance Spectroscopy Data Using the Distribution of Relaxation Times: A Bayesian and Hierarchical Bayesian Approach Hit paper breakdown → | 2015 | 546 |
| 11 | A Critical Review of Machine Learning of Energy Materials Hit paper breakdown → | 2020 | 484 |
| 12 | Optimal Regularization in Distribution of Relaxation Times applied to Electrochemical Impedance Spectroscopy: Ridge and Lasso Regression Methods - A Theoretical and Experimental Study Hit paper breakdown → | 2014 | 330 |
| 13 | 2019 | 330 | |
| 14 | 2014 | 294 | |
| 15 | 2017 | 275 | |
| 16 | 2003 | 268 | |
| 17 | 2020 | 259 | |
| 18 | 2021 | 234 | |
| 19 | Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy Hit paper breakdown → | 2020 | 212 |
| 20 | 2019 | 206 |
About Chi Chen
Chi Chen is a scholar working on Renewable Energy, Sustainability and the Environment, Materials Chemistry, Electrical and Electronic Engineering, Mechanical Engineering and Electronic, Optical and Magnetic Materials, having authored 234 papers that have together received 17.6k indexed citations. Recurring topics across this work include Electrocatalysts for Energy Conversion (37 papers), Advanced battery technologies research (25 papers), Machine Learning in Materials Science (25 papers), Fuel Cells and Related Materials (23 papers), Advancements in Battery Materials (22 papers), Advanced Battery Materials and Technologies (21 papers), Hydraulic Fracturing and Reservoir Analysis (19 papers) and Drilling and Well Engineering (14 papers). The work is most often cited by research in Renewable Energy, Sustainability and the Environment (5.9k citations), Materials Chemistry (8.5k citations), Catalysis (1.0k citations), Electrical and Electronic Engineering (8.4k citations) and Electrochemistry (660 citations). Chi Chen has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Francesco Ciucci, Shyue Ping Ong, Mattia Saccoccio, Ting Hei Wan, Yunxing Zuo, Weike Ye, Dengjie Chen, Xiangguo Li, Zheng Chen and Zhi Deng. Their work appears in journals such as npj Computational Materials, Journal of Materials Chemistry A, Chemistry of Materials, Thin Solid Films and Electrochimica Acta.
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.