Ying Wai Li

1.3k total citations
46 papers, 826 citations indexed

About

Ying Wai Li is a scholar working on Materials Chemistry, Condensed Matter Physics and Molecular Biology. According to data from OpenAlex, Ying Wai Li has authored 46 papers receiving a total of 826 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Materials Chemistry, 17 papers in Condensed Matter Physics and 15 papers in Molecular Biology. Recurrent topics in Ying Wai Li's work include Theoretical and Computational Physics (15 papers), Protein Structure and Dynamics (12 papers) and Machine Learning in Materials Science (10 papers). Ying Wai Li is often cited by papers focused on Theoretical and Computational Physics (15 papers), Protein Structure and Dynamics (12 papers) and Machine Learning in Materials Science (10 papers). Ying Wai Li collaborates with scholars based in United States, Switzerland and China. Ying Wai Li's co-authors include Thomas Wüst, D. P. Landau, Thomas Vogel, Kipton Barros, Nicholas Lubbers, Benjamin Nebgen, Sergei Tretiak, Justin S. Smith, Maksim Kulichenko and Richard A. Messerly and has published in prestigious journals such as Chemical Reviews, Physical Review Letters and The Journal of Chemical Physics.

In The Last Decade

Ying Wai Li

42 papers receiving 812 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ying Wai Li United States 15 452 212 161 139 117 46 826
Petr Plecháč United States 15 327 0.7× 127 0.6× 110 0.7× 148 1.1× 96 0.8× 56 697
Apala Majumdar United Kingdom 16 132 0.3× 218 1.0× 92 0.6× 96 0.7× 37 0.3× 69 929
Bruce M. Forrest Germany 13 340 0.8× 431 2.0× 107 0.7× 163 1.2× 33 0.3× 22 952
Eugene C. Gartland United States 17 158 0.3× 104 0.5× 151 0.9× 253 1.8× 147 1.3× 50 1.1k
Thomas Prellberg Australia 18 326 0.7× 590 2.8× 107 0.7× 192 1.4× 75 0.6× 80 1.0k
Florian Theil United Kingdom 12 419 0.9× 74 0.3× 50 0.3× 95 0.7× 240 2.1× 25 972
Tetsuji Tokihiro Japan 23 470 1.0× 274 1.3× 98 0.6× 427 3.1× 244 2.1× 95 1.9k
Tetsuo Deguchi Japan 24 196 0.4× 226 1.1× 157 1.0× 573 4.1× 119 1.0× 115 1.8k
Jeremy Schofield Canada 21 559 1.2× 165 0.8× 162 1.0× 552 4.0× 34 0.3× 74 1.3k
Maxim Dolgushev Germany 15 118 0.3× 65 0.3× 246 1.5× 107 0.8× 49 0.4× 46 595

Countries citing papers authored by Ying Wai Li

Since Specialization
Citations

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

Fields of papers citing papers by Ying Wai Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ying Wai Li. 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 Ying Wai Li. The network helps show where Ying Wai Li may publish in the future.

Co-authorship network of co-authors of Ying Wai Li

This figure shows the co-authorship network connecting the top 25 collaborators of Ying Wai Li. A scholar is included among the top collaborators of Ying Wai Li 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 Ying Wai Li. Ying Wai Li 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.
Fedik, Nikita, et al.. (2025). Challenges and opportunities for machine learning potentials in transition path sampling: alanine dipeptide and azobenzene studies. Digital Discovery. 4(5). 1158–1175. 3 indexed citations
2.
Zhang, Hao, Steven Hahn, Daniel M. Pajerowski, et al.. (2025). Sunny.jl: A Julia Package for Spin Dynamics. The Journal of Open Source Software. 10(116). 8138–8138. 1 indexed citations
3.
Li, Ying Wai, et al.. (2024). Digit classification of ghost imaging based on similarity measures. Optics & Laser Technology. 175. 110769–110769. 2 indexed citations
4.
Shanker, Apoorv, Ying Wai Li, Kristen M. Wilding, et al.. (2024). Quantification of heterogeneity in human CD8+ T cell responses to vaccine antigens: an HLA-guided perspective. Frontiers in Immunology. 15. 1420284–1420284. 1 indexed citations
5.
Fedik, Nikita, Benjamin Nebgen, Nicholas Lubbers, et al.. (2023). Synergy of semiempirical models and machine learning in computational chemistry. The Journal of Chemical Physics. 159(11). 12 indexed citations
6.
Tkachenko, Nikolay V., et al.. (2022). Performance Analysis of CP2K Code for Ab Initio Molecular Dynamics on CPUs and GPUs. Journal of Chemical Information and Modeling. 62(10). 2378–2386. 8 indexed citations
7.
Fedik, Nikita, R.I. Zubatyuk, Maksim Kulichenko, et al.. (2022). Extending machine learning beyond interatomic potentials for predicting molecular properties. Nature Reviews Chemistry. 6(9). 653–672. 94 indexed citations
8.
Kulichenko, Maksim, Justin S. Smith, Benjamin Nebgen, et al.. (2021). The Rise of Neural Networks for Materials and Chemical Dynamics. The Journal of Physical Chemistry Letters. 12(26). 6227–6243. 66 indexed citations
9.
Barros, Kipton, J. Haack, Christoph Junghans, et al.. (2020). Multiscale simulation of plasma flows using active learning. Physical review. E. 102(2). 23310–23310. 12 indexed citations
10.
Samarakoon, Anjana, Kipton Barros, Ying Wai Li, et al.. (2019). Machine Learning Assisted Insight to Spin Ice Dy$_2$Ti$_2$O$_7$. arXiv (Cornell University). 2 indexed citations
11.
Li, Ying Wai, et al.. (2019). Accelerating DCA++ (Dynamical Cluster Approximation) Scientific Application on the Summit Supercomputer. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 5 indexed citations
12.
Yuk, Simuck F., Krishna Chaitanya Pitike, Serge Nakhmanson, et al.. (2017). Towards an accurate description of perovskite ferroelectrics: exchange and correlation effects. Scientific Reports. 7(1). 43482–43482. 69 indexed citations
13.
Li, Ying Wai, et al.. (2015). Effect of surface attractive strength on structural transitions of a confined HP lattice protein. Journal of Physics Conference Series. 640. 12015–12015. 1 indexed citations
14.
Vogel, Thomas, Ying Wai Li, Thomas Wüst, & D. P. Landau. (2014). Scalable replica-exchange framework for Wang-Landau sampling. Physical Review E. 90(2). 23302–23302. 51 indexed citations
15.
Vogel, Thomas, et al.. (2014). Effect of single-site mutations on hydrophobic-polar lattice proteins. Physical Review E. 90(3). 33307–33307. 17 indexed citations
16.
Eisenbach, Markus, Junqi Yin, Don M. Nicholson, & Ying Wai Li. (2013). First principles calculation of finite temperature magnetism in Ni. Bulletin of the American Physical Society. 2013. 1 indexed citations
17.
Li, Ying Wai, Thomas Wüst, & D. P. Landau. (2013). Generic folding and transition hierarchies for surface adsorption of hydrophobic-polar lattice model proteins. Physical Review E. 87(1). 12706–12706. 36 indexed citations
18.
Vogel, Thomas, Ying Wai Li, Thomas Wüst, & D. P. Landau. (2013). Generic, Hierarchical Framework for Massively Parallel Wang-Landau Sampling. Physical Review Letters. 110(21). 210603–210603. 92 indexed citations
19.
Li, Ying Wai, Thomas Wüst, & D. P. Landau. (2012). Surface adsorption of lattice HP proteins: Thermodynamics and structural transitions using Wang-Landau sampling. Journal of Physics Conference Series. 402. 12046–12046. 2 indexed citations
20.
Li, Ying Wai, Thomas Wüst, & D. P. Landau. (2011). Monte Carlo simulations of the HP model (the “Ising model” of protein folding). Computer Physics Communications. 182(9). 1896–1899. 27 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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