Steph-Yves Louis

553 total citations
9 papers, 387 citations indexed

About

Steph-Yves Louis is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Automotive Engineering. According to data from OpenAlex, Steph-Yves Louis has authored 9 papers receiving a total of 387 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Materials Chemistry, 2 papers in Computational Theory and Mathematics and 1 paper in Automotive Engineering. Recurrent topics in Steph-Yves Louis's work include Machine Learning in Materials Science (9 papers), X-ray Diffraction in Crystallography (6 papers) and Computational Drug Discovery Methods (2 papers). Steph-Yves Louis is often cited by papers focused on Machine Learning in Materials Science (9 papers), X-ray Diffraction in Crystallography (6 papers) and Computational Drug Discovery Methods (2 papers). Steph-Yves Louis collaborates with scholars based in United States, China and Sri Lanka. Steph-Yves Louis's co-authors include Jianjun Hu, Yong Zhao, Yuqi Song, Alireza Nasiri, Fei Liu, Xiran Wang, Sadman Sadeed Omee, Edirisuriya M. Dilanga Siriwardane, Rongzhi Dong and Nihang Fu and has published in prestigious journals such as ACS Applied Materials & Interfaces, The Journal of Physical Chemistry C and Physical Chemistry Chemical Physics.

In The Last Decade

Steph-Yves Louis

9 papers receiving 378 citations

Peers

Steph-Yves Louis
Rhys E. A. Goodall United Kingdom
Kevin Decker United States
Qiaohao Liang United States
Felix Brockherde Germany
Janosh Riebesell United States
Sterling G. Baird United States
Sadman Sadeed Omee United States
Anthony Wang Germany
David Milsted United States
Frederick Webber United States
Rhys E. A. Goodall United Kingdom
Steph-Yves Louis
Citations per year, relative to Steph-Yves Louis Steph-Yves Louis (= 1×) peers Rhys E. A. Goodall

Countries citing papers authored by Steph-Yves Louis

Since Specialization
Citations

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

Fields of papers citing papers by Steph-Yves Louis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Steph-Yves Louis. 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 Steph-Yves Louis. The network helps show where Steph-Yves Louis may publish in the future.

Co-authorship network of co-authors of Steph-Yves Louis

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

All Works

9 of 9 papers shown
1.
Omee, Sadman Sadeed, et al.. (2022). Scalable deeper graph neural networks for high-performance materials property prediction. Patterns. 3(5). 100491–100491. 74 indexed citations
2.
Louis, Steph-Yves, Edirisuriya M. Dilanga Siriwardane, Rajendra P. Joshi, et al.. (2022). Accurate Prediction of Voltage of Battery Electrode Materials Using Attention-Based Graph Neural Networks. ACS Applied Materials & Interfaces. 14(23). 26587–26594. 38 indexed citations
3.
Louis, Steph-Yves, et al.. (2022). NODE-SELECT: A graph neural network based on a selective propagation technique. Neurocomputing. 494. 396–408. 7 indexed citations
4.
Nguyen, Nghia T., et al.. (2022). Predicting Lattice Vibrational Frequencies Using Deep Graph Neural Networks. ACS Omega. 7(30). 26641–26649. 7 indexed citations
5.
Hu, Jianjun, Yuqi Song, Sadman Sadeed Omee, et al.. (2022). MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art. npj Computational Materials. 8(1). 39 indexed citations
6.
Xin, Rui, Edirisuriya M. Dilanga Siriwardane, Yuqi Song, et al.. (2021). Active-Learning-Based Generative Design for the Discovery of Wide-Band-Gap Materials. The Journal of Physical Chemistry C. 125(29). 16118–16128. 16 indexed citations
7.
Louis, Steph-Yves, Yong Zhao, Alireza Nasiri, et al.. (2020). Graph convolutional neural networks with global attention for improved materials property prediction. Physical Chemistry Chemical Physics. 22(32). 18141–18148. 160 indexed citations
8.
Zhao, Yong, et al.. (2020). Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks. The Journal of Physical Chemistry C. 124(31). 17262–17273. 25 indexed citations
9.
Song, Yuqi, Yong Zhao, Alireza Nasiri, et al.. (2020). Machine Learning based prediction of noncentrosymmetric crystal materials. Computational Materials Science. 183. 109792–109792. 21 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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2026