Pinghui Mo

690 total citations
8 papers, 90 citations indexed

About

Pinghui Mo is a scholar working on Electrical and Electronic Engineering, Materials Chemistry and Electronic, Optical and Magnetic Materials. According to data from OpenAlex, Pinghui Mo has authored 8 papers receiving a total of 90 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Electrical and Electronic Engineering, 7 papers in Materials Chemistry and 2 papers in Electronic, Optical and Magnetic Materials. Recurrent topics in Pinghui Mo's work include Machine Learning in Materials Science (4 papers), 2D Materials and Applications (3 papers) and Advanced Memory and Neural Computing (3 papers). Pinghui Mo is often cited by papers focused on Machine Learning in Materials Science (4 papers), 2D Materials and Applications (3 papers) and Advanced Memory and Neural Computing (3 papers). Pinghui Mo collaborates with scholars based in China and United States. Pinghui Mo's co-authors include Jiwu Lu, Jie Liu, Dan Zhao, Chang Li, Danying Gao, Ming Tao and Xin Zhang and has published in prestigious journals such as Journal of Applied Physics, IEEE Electron Device Letters and IEEE Transactions on Circuits and Systems I Regular Papers.

In The Last Decade

Pinghui Mo

8 papers receiving 89 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pinghui Mo China 6 67 42 16 14 11 8 90
William J. Baldwin United Kingdom 4 78 1.2× 50 1.2× 11 0.7× 10 0.7× 4 0.4× 8 93
Mohammad Hossein Bani-Hashemian Switzerland 5 83 1.2× 84 2.0× 6 0.4× 34 2.4× 5 0.5× 10 122
W. Chang Taiwan 4 77 1.1× 32 0.8× 47 2.9× 17 1.2× 6 0.5× 4 91
Sanyum Channa United States 5 40 0.6× 22 0.5× 18 1.1× 55 3.9× 13 1.2× 7 72
Florian Margot Switzerland 4 31 0.5× 16 0.4× 14 0.9× 9 0.6× 11 1.0× 4 48
L. Jansen France 3 32 0.5× 14 0.3× 29 1.8× 5 0.4× 5 0.5× 7 63
Y. C. Yang China 7 42 0.6× 15 0.4× 9 0.6× 20 1.4× 5 0.5× 9 86
Fatima Alarab Switzerland 4 29 0.4× 12 0.3× 15 0.9× 8 0.6× 8 0.7× 11 39
J. Lieb United States 3 27 0.4× 29 0.7× 4 0.3× 15 1.1× 6 0.5× 8 76
M. X. Wang United Kingdom 4 63 0.9× 31 0.7× 11 0.7× 24 1.7× 13 1.2× 5 71

Countries citing papers authored by Pinghui Mo

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Pinghui Mo

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

All Works

8 of 8 papers shown
1.
Mo, Pinghui, et al.. (2023). A Heterogeneous Parallel Non-von Neumann Architecture System for Accurate and Efficient Machine Learning Molecular Dynamics. IEEE Transactions on Circuits and Systems I Regular Papers. 70(6). 2439–2449. 1 indexed citations
2.
Mo, Pinghui, et al.. (2022). Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture. npj Computational Materials. 8(1). 24 indexed citations
3.
Mo, Pinghui, et al.. (2020). Deep Neural Network for Accurate and Efficient Atomistic Modeling of Phase Change Memory. IEEE Electron Device Letters. 41(3). 365–368. 16 indexed citations
4.
Mo, Pinghui, et al.. (2020). Transfer Learning of Potential Energy Surfaces for Efficient Atomistic Modeling of Doping and Alloy. IEEE Electron Device Letters. 41(4). 633–636. 6 indexed citations
5.
Mo, Pinghui, et al.. (2019). Strain-enhanced electron mobility and mobility anisotropy in a two-dimensional vanadium diselenide monolayer. Journal of Applied Physics. 126(4). 6 indexed citations
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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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Breakdown of academic impact, for papers by William J. Baldwin Breakdown of academic impact, for papers by Mohammad Hossein Bani-Hashemian Breakdown of academic impact, for papers by W. Chang Breakdown of academic impact, for papers by Sanyum Channa Breakdown of academic impact, for papers by Florian Margot Breakdown of academic impact, for papers by L. Jansen Breakdown of academic impact, for papers by Y. C. Yang Breakdown of academic impact, for papers by Fatima Alarab Breakdown of academic impact, for papers by J. Lieb Breakdown of academic impact, for papers by M. X. Wang
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2026