Pinghui Mo
Impact in
- Materials Chemistry top 10%
- Machine Learning in Materials Science
- 2D Materials and Applications
- Electronic and Structural Properties of Oxides
Papers in
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- Machine Learning in Materials Science 9
- 2D Materials and Applications 3
- Phase-change materials and chalcogenides 2
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- Advanced Memory and Neural Computing 4
- Ferroelectric and Negative Capacitance Devices 3
- Fuel Cells and Related Materials 2
- Co-authors
- Jiwu Lu (4 shared papers)Jie Liu (1 shared paper)Dan Zhao (2 shared papers)Chang Li (1 shared paper)Danying Gao (1 shared paper)Ming Tao (1 shared paper)Xin Zhang (3 shared papers)Jie Liu (2 shared papers)
- Journals
- IEEE Electron Device Letters (2 papers)Journal of Chemical Theory and Computation (2 papers)Journal of Applied Physics (2 papers)npj Computational Materials (2 papers)IEEE Journal of the Electron Devices Society (1 paper)
- Partner nations
- ChinaUnited StatesSwitzerland
In The Last Decade
Pinghui Mo
11 papers receiving 570 citations
Pinghui Mo's Hit Papers
Peers
Comparison fields: 5 of 60
- Materials Chemistry 389
- Catalysis 27
- Computational Theory and Mathematics 52
- Atomic and Molecular Physics, and Optics 92
- Physical and Theoretical Chemistry 21
Countries citing papers authored by Pinghui Mo
This map shows the geographic impact of Pinghui Mo'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 Pinghui Mo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pinghui Mo more than expected).
Fields of papers citing papers by Pinghui Mo
This network shows the impact of papers produced by Pinghui Mo. 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 Pinghui Mo. The network helps show where Pinghui Mo may publish in the future.
Co-authors
The 10 scholars most cited alongside Pinghui Mo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | DeePMD-kit v2: A software package for deep potential models Hit paper breakdown → | 2023 | 417 |
| 2 | DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials Hit paper breakdown → | 2025 | 48 |
| 3 | 2022 | 38 | |
| 4 | 2018 | 26 | |
| 5 | 2020 | 21 | |
| 6 | 2018 | 11 | |
| 7 | 2020 | 7 | |
| 8 | 2024 | 7 | |
| 9 | 2019 | 6 | |
| 10 | 2023 | 4 | |
| 11 | 2018 | 1 | |
| 12 | 2025 | 0 | |
| 13 | 2025 | 0 |
About Pinghui Mo
Pinghui Mo is a scholar working on Materials Chemistry, Electrical and Electronic Engineering, Computational Theory and Mathematics, Electronic, Optical and Magnetic Materials and Biomedical Engineering, having authored 13 papers that have together received 586 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (9 papers), Advanced Memory and Neural Computing (4 papers), 2D Materials and Applications (3 papers), Ferroelectric and Negative Capacitance Devices (3 papers), Fuel Cells and Related Materials (2 papers), Heusler alloys: electronic and magnetic properties (2 papers), Phase-change materials and chalcogenides (2 papers) and Computational Drug Discovery Methods (2 papers). The work is most often cited by research in Materials Chemistry (389 citations), Catalysis (27 citations), Computational Theory and Mathematics (52 citations), Atomic and Molecular Physics, and Optics (92 citations) and Physical and Theoretical Chemistry (21 citations). Pinghui Mo has collaborated with scholars based in China, United States and Switzerland. Frequent co-authors include Jiwu Lu, Jie Liu, Dan Zhao, Chang Li, Danying Gao, Ming Tao, Xin Zhang, Jie Liu, Guang Yang and Xinyu Xiao. Their work appears in journals such as IEEE Electron Device Letters, Journal of Chemical Theory and Computation, Journal of Applied Physics, npj Computational Materials and IEEE Journal of the Electron Devices Society.
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.