Simon Wengert

543 total citations
10 papers, 386 citations indexed

About

Simon Wengert is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Physical and Theoretical Chemistry. According to data from OpenAlex, Simon Wengert has authored 10 papers receiving a total of 386 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Materials Chemistry, 4 papers in Computational Theory and Mathematics and 3 papers in Physical and Theoretical Chemistry. Recurrent topics in Simon Wengert's work include Machine Learning in Materials Science (6 papers), Computational Drug Discovery Methods (4 papers) and Crystallography and molecular interactions (3 papers). Simon Wengert is often cited by papers focused on Machine Learning in Materials Science (6 papers), Computational Drug Discovery Methods (4 papers) and Crystallography and molecular interactions (3 papers). Simon Wengert collaborates with scholars based in Germany, United Kingdom and United States. Simon Wengert's co-authors include Karsten Reuter, Johannes T. Margraf, Gábor Cśanyi, Heike Boehm, Christian Künkel, Ann‐Kathrin Huber, Noam Bernstein, Tamás K. Stenczel, Bingqing Cheng and Ryan‐Rhys Griffiths and has published in prestigious journals such as The Journal of Chemical Physics, Accounts of Chemical Research and Advanced Energy Materials.

In The Last Decade

Simon Wengert

9 papers receiving 382 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Simon Wengert Germany	8	246	87	76	63	49	10	386
Takayuki Kimura Japan	13	230 0.9×	144 1.7×	37 0.5×	120 1.9×	7 0.1×	46	578
Brandon Walker United States	9	166 0.7×	51 0.6×	64 0.8×	314 5.0×	20 0.4×	15	681
Shibom Basu France	13	294 1.2×	39 0.4×	13 0.2×	327 5.2×	37 0.8×	30	631
Azuma Matsuura Japan	10	147 0.6×	167 1.9×	48 0.6×	158 2.5×	4 0.1×	21	418
Nalinda P. Wickramasinghe United States	17	393 1.6×	48 0.6×	8 0.1×	234 3.7×	97 2.0×	23	1.0k
Siddhartha Sankar Ghosh India	12	138 0.6×	59 0.7×	15 0.2×	142 2.3×	19 0.4×	55	539
Takahiro Shirai Japan	13	189 0.8×	174 2.0×	4 0.1×	86 1.4×	18 0.4×	37	550
Wengang Zhang United States	14	222 0.9×	12 0.1×	28 0.4×	99 1.6×	20 0.4×	25	415
J. Ohana France	10	315 1.3×	21 0.2×	11 0.1×	297 4.7×	23 0.5×	18	559

Countries citing papers authored by Simon Wengert

Since Specialization

Citations

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

Fields of papers citing papers by Simon Wengert

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Simon Wengert. 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 Simon Wengert. The network helps show where Simon Wengert may publish in the future.

Co-authorship network of co-authors of Simon Wengert

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

All Works

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10 of 10 papers shown

Grigorev, Petr, Lucas Frérot, Andreas Klemenz, et al.. (2024). matscipy: materials science at the atomic scale withPython. The Journal of Open Source Software. 9(93). 5668–5668. 10 indexed citations

Wengert, Simon, Tamás K. Stenczel, Hendrik H. Heenen, et al.. (2023). wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows. The Journal of Chemical Physics. 159(12). 9 indexed citations

Wengert, Simon, et al.. (2022). Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model. Machine Learning Science and Technology. 3(1). 15032–15032. 28 indexed citations

Wengert, Simon, Gábor Cśanyi, Karsten Reuter, & Johannes T. Margraf. (2022). A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings. Journal of Chemical Theory and Computation. 18(7). 4586–4593. 28 indexed citations

Schierholz, Roland, Ivan Povstugar, Juri Barthel, et al.. (2021). Nano‐Scale Complexions Facilitate Li Dendrite‐Free Operation in LATP Solid‐State Electrolyte. Advanced Energy Materials. 11(26). 62 indexed citations

Wengert, Simon, Gábor Cśanyi, Karsten Reuter, & Johannes T. Margraf. (2021). Data-efficient machine learning for molecular crystal structure prediction. Chemical Science. 12(12). 4536–4546. 77 indexed citations

Schierholz, Roland, Ivan Povstugar, Juri Barthel, et al.. (2021). All‐Solid‐State Batteries: Nano‐Scale Complexions Facilitate Li Dendrite‐Free Operation in LATP Solid‐State Electrolyte (Adv. Energy Mater. 26/2021). Advanced Energy Materials. 11(26).

Cheng, Bingqing, Ryan‐Rhys Griffiths, Simon Wengert, et al.. (2020). Mapping Materials and Molecules. Accounts of Chemical Research. 53(9). 1981–1991. 89 indexed citations

Wengert, Simon, et al.. (2017). Smooth and rapid microwave synthesis of MIL-53(Fe) including superparamagnetic γ-Fe2O3 nanoparticles. Journal of Magnetism and Magnetic Materials. 444. 168–172. 3 indexed citations

10.

Huber, Ann‐Kathrin, et al.. (2017). A Trickster in Disguise: Hyaluronan’s Ambivalent Roles in the Matrix. Frontiers in Oncology. 7. 242–242. 80 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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