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.
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
20221.0k citationsSimon Batzner, Albert Musaelian et al.Nature Communicationsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Mario Geiger'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 Mario Geiger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mario Geiger more than expected).
This network shows the impact of papers produced by Mario Geiger. 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 Mario Geiger. The network helps show where Mario Geiger may publish in the future.
Co-authorship network of co-authors of Mario Geiger
This figure shows the co-authorship network connecting the top 25 collaborators of Mario Geiger.
A scholar is included among the top collaborators of Mario Geiger 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 Mario Geiger. Mario Geiger is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Geiger, Mario, Tess Smidt, B. Miller, et al.. (2021). e3nn/e3nn: 2021-06-21. Zenodo (CERN European Organization for Nuclear Research).1 indexed citations
Geiger, Mario, Stefano Spigler, Arthur Paul Jacot, & Matthieu Wyart. (2019). Disentangling feature and lazy learning in deep neural networks: an empirical study.. arXiv (Cornell University).3 indexed citations
11.
Baity‐Jesi, Marco, Levent Sagun, Mario Geiger, et al.. (2018). Comparing dynamics: deep neural networks versus glassy systems. IRIS Research product catalog (Sapienza University of Rome).22 indexed citations
Weiler, Maurice, Mario Geiger, Max Welling, Wouter Boomsma, & Taco Cohen. (2018). 3D steerable CNNs: learning rotationally equivariant features in volumetric data. UvA-DARE (University of Amsterdam). 31. 10402–10413.78 indexed citations
Taylor, Chris, Susan Astley, Caroline Boggis, et al.. (1996). A statistical representation of pattern structure for digital mammography.. Research Explorer (The University of Manchester).1 indexed citations
19.
Astley, Sue, Chris Taylor, Caroline Boggis, et al.. (1996). Model based classification of linear structures for digital mammograms. Research Explorer (The University of Manchester).4 indexed citations
20.
Maher, John F., Louis R. Lapierre, George E. Schreiner, Mario Geiger, & Frederic B. Westervelt. (1963). Regional Heparinization for Hemodialysis. New England Journal of Medicine. 268(9). 451–456.58 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.