Peter Staar

1.3k total citations · 1 hit paper
33 papers, 703 citations indexed

About

Peter Staar is a scholar working on Condensed Matter Physics, Atomic and Molecular Physics, and Optics and Artificial Intelligence. According to data from OpenAlex, Peter Staar has authored 33 papers receiving a total of 703 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Condensed Matter Physics, 9 papers in Atomic and Molecular Physics, and Optics and 8 papers in Artificial Intelligence. Recurrent topics in Peter Staar's work include Physics of Superconductivity and Magnetism (11 papers), Quantum and electron transport phenomena (8 papers) and Advanced Condensed Matter Physics (6 papers). Peter Staar is often cited by papers focused on Physics of Superconductivity and Magnetism (11 papers), Quantum and electron transport phenomena (8 papers) and Advanced Condensed Matter Physics (6 papers). Peter Staar collaborates with scholars based in Switzerland, United States and United Kingdom. Peter Staar's co-authors include T. C. Schulthess, Thomas Maier, Alessandro Curioni, Teodoro Laino, Edward O. Pyzer‐Knapp, John R. Smith, James Sexton, Jed W. Pitera, Daniel P. Sanders and Seiji Takeda and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Physical Review B.

In The Last Decade

Peter Staar

31 papers receiving 681 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Accelerating materials discovery using artificial intelligence, high performance computing and robotics

2022 241 citations Edward O. Pyzer‐Knapp, Peter Staar et al. npj Computational Materials profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Peter Staar Switzerland	13	200	161	135	88	76	33	703
Dongmei Wu China	15	170 0.8×	230 1.4×	267 2.0×	255 2.9×	43 0.6×	78	819
Matthias Neubauer Austria	17	80 0.4×	219 1.4×	92 0.7×	84 1.0×	101 1.3×	69	691
Maria Longobardi Italy	20	89 0.4×	302 1.9×	114 0.8×	133 1.5×	126 1.7×	60	1.5k
Xin Gong China	20	64 0.3×	322 2.0×	161 1.2×	197 2.2×	84 1.1×	58	1.2k
Yu Yang China	16	71 0.4×	83 0.5×	31 0.2×	47 0.5×	88 1.2×	76	968
Petr Plecháč United States	15	127 0.6×	327 2.0×	148 1.1×	40 0.5×	35 0.5×	56	697
Wen Shen China	18	96 0.5×	415 2.6×	168 1.2×	142 1.6×	159 2.1×	67	1.0k
Guolin Yu China	15	388 1.9×	315 2.0×	308 2.3×	180 2.0×	120 1.6×	106	1.1k
Kunpeng Wang China	17	47 0.2×	232 1.4×	260 1.9×	112 1.3×	208 2.7×	72	811
Hiroshi Akiba Japan	15	52 0.3×	181 1.1×	77 0.6×	113 1.3×	20 0.3×	52	608

Countries citing papers authored by Peter Staar

Since Specialization

Citations

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

Fields of papers citing papers by Peter Staar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peter Staar

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

All Works

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20 of 20 papers shown

1.

Pyzer‐Knapp, Edward O., Matteo Manica, Peter Staar, et al.. (2025). Foundation models for materials discovery – current state and future directions. npj Computational Materials. 11(1). 20 indexed citations

2.

Weber, Valéry, et al.. (2025). MarkushGrapher: Joint Visual and Textual Recognition of Markush Structures. arXiv (Cornell University). 14505–14515.

3.

Mishra, Lokesh, et al.. (2024). Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs. 193–214. 2 indexed citations

4.

Mishra, Lokesh, et al.. (2024). ESG Accountability Made Easy: DocQA at Your Service. Proceedings of the AAAI Conference on Artificial Intelligence. 38(21). 23814–23816. 2 indexed citations

5.

Stein, Lina, S. Karthik Mukkavilli, Peter Staar, et al.. (2024). Wealth Over Woe: Global Biases in Hydro‐Hazard Research. Earth s Future. 12(10). 8 indexed citations

6.

Weber, Valéry, et al.. (2024). PatCID: an open-access dataset of chemical structures in patent documents. Nature Communications. 15(1). 6532–6532. 6 indexed citations

7.

Tadesse, Girmaw Abebe, Celia Cintas, Kush R. Varshney, et al.. (2023). Skin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning. npj Digital Medicine. 6(1). 151–151. 12 indexed citations

8.

Danelljan, Martin, et al.. (2023). MolGrapher: Graph-based Visual Recognition of Chemical Structures. 19495–19504. 6 indexed citations

9.

Fusco, Francesco, et al.. (2023). pNLP-Mixer: an Efficient all-MLP Architecture for Language. 53–60. 13 indexed citations

10.

Fusco, Francesco, et al.. (2022). Unsupervised Term Extraction for Highly Technical Domains. 1–8. 2 indexed citations

11.

Staar, Peter, et al.. (2020). Corpus processing service: A Knowledge Graph platform to perform deep data exploration on corpora. SHILAP Revista de lepidopterología. 1(2). 7 indexed citations

12.

Staar, Peter, et al.. (2019). Application of Geocognitive Technologies to Basin & Petroleum System Analyses.

13.

Giefers, Heiner, Peter Staar, Costas Bekas, & Christoph Hagleitner. (2016). Analyzing the energy-efficiency of sparse matrix multiplication on heterogeneous systems: A comparative study of GPU, Xeon Phi and FPGA. 46–56. 12 indexed citations

14.

Maier, Thomas, Peter Staar, Vivek Mishra, et al.. (2016). Pairing in a dry Fermi sea. Nature Communications. 7(1). 11875–11875. 18 indexed citations

15.

Georgakoudis, Giorgis, Charles J. Gillan, Ivor Spence, et al.. (2016). NanoStreams: Codesigned microservers for edge analytics in real time. Research Portal (Queen's University Belfast). 180–187. 3 indexed citations

16.

Griffin, Sinéad M., Peter Staar, T. C. Schulthess, Matthias Troyer, & Nicola A. Spaldin. (2016). A bespoke single-band Hubbard model material. Physical review. B.. 93(7). 2 indexed citations

17.

Rudi, Johann, A. Cristiano I. Malossi, Tobin Isaac, et al.. (2015). An extreme-scale implicit solver for complex PDEs. CaltechAUTHORS (California Institute of Technology). 1–12. 80 indexed citations

18.

Staar, Peter, Thomas Maier, & T. C. Schulthess. (2013). DCA$^+$: Incorporating self-consistently a continuous momentum self-energy in the Dynamical Cluster Approximation. Bulletin of the American Physical Society. 2013. 1 indexed citations

19.

Staar, Peter, Thomas Maier, & T. C. Schulthess. (2013). Dynamical cluster approximation with continuous lattice self-energy. Physical Review B. 88(11). 29 indexed citations

20.

Staar, Peter, Thomas Maier, & T. C. Schulthess. (2012). Efficient non-equidistant FFT approach to the measurement of single- and two-particle quantities in continuous time Quantum Monte Carlo methods. Journal of Physics Conference Series. 402. 12015–12015. 8 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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