Jonas Lederer

559 total citations
9 papers, 341 citations indexed

About

Jonas Lederer is a scholar working on Materials Chemistry, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Jonas Lederer has authored 9 papers receiving a total of 341 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Materials Chemistry, 5 papers in Artificial Intelligence and 2 papers in Computational Theory and Mathematics. Recurrent topics in Jonas Lederer's work include Machine Learning in Materials Science (7 papers), Explainable Artificial Intelligence (XAI) (4 papers) and Advanced Graph Neural Networks (2 papers). Jonas Lederer is often cited by papers focused on Machine Learning in Materials Science (7 papers), Explainable Artificial Intelligence (XAI) (4 papers) and Advanced Graph Neural Networks (2 papers). Jonas Lederer collaborates with scholars based in Germany, South Korea and Japan. Jonas Lederer's co-authors include Kristof T. Schütt, Grégoire Montavon, Shinichi Nakajima, Oliver Eberle, Klaus-Robert Müller, Klaus‐Robert Müller, Michael Gastegger, Niklas W. A. Gebauer, Alessio Gagliardi and Waldemar Kaiser and has published in prestigious journals such as Nature Communications, The Journal of Chemical Physics and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Jonas Lederer

9 papers receiving 336 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jonas Lederer Germany	7	166	129	58	57	28	9	341
Chengqiang Lu China	9	135 0.8×	126 1.0×	62 1.1×	112 2.0×	66 2.4×	17	315
Gyoung S. Na South Korea	9	86 0.5×	203 1.6×	52 0.9×	49 0.9×	18 0.6×	26	318
Chee‐Kong Lee United States	11	241 1.5×	63 0.5×	41 0.7×	52 0.9×	19 0.7×	13	349
P. Balamurugan India	8	71 0.4×	77 0.6×	28 0.5×	41 0.7×	23 0.8×	70	296
Xiangwei Liu China	11	56 0.3×	114 0.9×	91 1.6×	24 0.4×	85 3.0×	56	322
Alexander Smith United States	11	66 0.4×	88 0.7×	204 3.5×	223 3.9×	22 0.8×	30	582
Sam Cox United States	3	73 0.4×	189 1.5×	22 0.4×	77 1.4×	59 2.1×	4	340
Woosuk Lee South Korea	13	194 1.2×	119 0.9×	131 2.3×	56 1.0×	25 0.9×	27	510
Alexander Semenov Russia	8	60 0.4×	34 0.3×	32 0.6×	37 0.6×	28 1.0×	50	157

Countries citing papers authored by Jonas Lederer

Since Specialization

Citations

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

Fields of papers citing papers by Jonas Lederer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonas Lederer

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

All Works

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9 of 9 papers shown

Lederer, Jonas, et al.. (2025). Analyzing Atomic Interactions in Molecules as Learned by Neural Networks. Journal of Chemical Theory and Computation. 21(2). 714–729. 6 indexed citations

Lederer, Jonas, et al.. (2025). Towards symbolic XAI — explanation through human understandable logical relationships between features. Information Fusion. 118. 102923–102923. 2 indexed citations

Lederer, Jonas, et al.. (2025). Peering inside the black box by learning the relevance of many-body functions in neural network potentials. Nature Communications. 16(1). 9898–9898. 1 indexed citations

Lederer, Jonas, Michael Gastegger, Kristof T. Schütt, et al.. (2023). Automatic identification of chemical moieties. Physical Chemistry Chemical Physics. 25(38). 26370–26379. 7 indexed citations

Schütt, Kristof T., et al.. (2023). SchNetPack 2.0: A neural network toolbox for atomistic machine learning. The Journal of Chemical Physics. 158(14). 144801–144801. 55 indexed citations

Letzgus, Simon, Patrick Wagner, Jonas Lederer, et al.. (2022). Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective. IEEE Signal Processing Magazine. 39(4). 40–58. 56 indexed citations

Eberle, Oliver, Jonas Lederer, Shinichi Nakajima, et al.. (2021). Higher-Order Explanations of Graph Neural Networks via Relevant Walks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(11). 7581–7596. 155 indexed citations

Eberle, Oliver, Jonas Lederer, Shinichi Nakajima, et al.. (2020). XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks. arXiv (Cornell University). 15 indexed citations

Lederer, Jonas, Waldemar Kaiser, Alessandro Mattoni, & Alessio Gagliardi. (2018). Machine Learning–Based Charge Transport Computation for Pentacene. Advanced Theory and Simulations. 2(2). 44 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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