Beatriz Seoane

1.5k total citations
28 papers, 405 citations indexed

About

Beatriz Seoane is a scholar working on Materials Chemistry, Condensed Matter Physics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Beatriz Seoane has authored 28 papers receiving a total of 405 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Materials Chemistry, 12 papers in Condensed Matter Physics and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Beatriz Seoane's work include Theoretical and Computational Physics (12 papers), Material Dynamics and Properties (9 papers) and Generative Adversarial Networks and Image Synthesis (7 papers). Beatriz Seoane is often cited by papers focused on Theoretical and Computational Physics (12 papers), Material Dynamics and Properties (9 papers) and Generative Adversarial Networks and Image Synthesis (7 papers). Beatriz Seoane collaborates with scholars based in Spain, France and Italy. Beatriz Seoane's co-authors include Giorgio Parisi, Francesco Zamponi, V. Martı́n-Mayor, L. A. Fernández, Hidetoshi Nishimori, Patrick Charbonneau, Yuliang Jin, Aurélien Decelle, P. Verrocchio and Ludovic Berthier and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Bioinformatics.

In The Last Decade

Beatriz Seoane

26 papers receiving 392 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Beatriz Seoane Spain 11 240 184 79 73 65 28 405
Saurish Chakrabarty United States 12 146 0.6× 125 0.7× 17 0.2× 109 1.5× 27 0.4× 15 303
Barbara Coluzzi Italy 9 229 1.0× 193 1.0× 9 0.1× 71 1.0× 106 1.6× 15 417
Jacobus A. van Meel Netherlands 6 169 0.7× 78 0.4× 10 0.1× 42 0.6× 53 0.8× 7 310
S. Kobe Germany 13 99 0.4× 289 1.6× 16 0.2× 125 1.7× 10 0.2× 41 410
Kristina van Duijvendijk France 4 113 0.5× 175 1.0× 40 0.5× 209 2.9× 33 0.5× 4 508
Etienne P. Bernard France 4 507 2.1× 491 2.7× 11 0.1× 118 1.6× 199 3.1× 4 743
Abhik Basu India 12 116 0.5× 254 1.4× 8 0.1× 71 1.0× 71 1.1× 65 550
B. Hübinger Germany 8 59 0.2× 162 0.9× 18 0.2× 161 2.2× 26 0.4× 11 446
Elisabeth Agoritsas Switzerland 12 173 0.7× 199 1.1× 6 0.1× 53 0.7× 55 0.8× 20 332
T.C. Newell United States 19 208 0.9× 36 0.2× 28 0.4× 1.3k 17.2× 79 1.2× 50 1.6k

Countries citing papers authored by Beatriz Seoane

Since Specialization
Citations

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

Fields of papers citing papers by Beatriz Seoane

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Beatriz Seoane

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

All Works

20 of 20 papers shown
1.
Decelle, Aurélien, et al.. (2025). Inferring Higher-Order Couplings with Neural Networks.. PubMed. 135(20). 207301–207301.
2.
Biroli, Giulio, et al.. (2025). Cascade of phase transitions in the training of energy-based models*. Journal of Statistical Mechanics Theory and Experiment. 2025(7). 74004–74004.
3.
Seoane, Beatriz, et al.. (2025). LoRA-DR-suite: adapted embeddings predict intrinsic and soft disorder from protein sequences. Bioinformatics. 41(Supplement_1). i439–i448. 1 indexed citations
4.
Decelle, Aurélien, et al.. (2024). Inferring effective couplings with restricted Boltzmann machines. SciPost Physics. 16(4). 6 indexed citations
5.
Carbone, Alessandra, et al.. (2024). Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium Physics. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(2). 1309–1316. 3 indexed citations
6.
Biroli, Giulio, et al.. (2024). Cascade of phase transitions in the training of energy-based models. arXiv (Cornell University). 55591–55619. 2 indexed citations
7.
Decelle, Aurélien, et al.. (2023). Unsupervised hierarchical clustering using the learning dynamics of restricted Boltzmann machines. Physical review. E. 108(1). 14110–14110. 10 indexed citations
8.
Decelle, Aurélien, et al.. (2023). Learning a restricted Boltzmann machine using biased Monte Carlo sampling. SciPost Physics. 14(3). 10 indexed citations
9.
Decelle, Aurélien, Cyril Furtlehner, & Beatriz Seoane. (2022). Equilibrium and non-equilibrium regimes in the learning of restricted Boltzmann machines*. Journal of Statistical Mechanics Theory and Experiment. 2022(11). 114009–114009. 11 indexed citations
10.
Seoane, Beatriz & Alessandra Carbone. (2022). Soft disorder modulates the assembly path of protein complexes. PLoS Computational Biology. 18(11). e1010713–e1010713. 6 indexed citations
11.
Seoane, Beatriz. (2021). A scaling approach to estimate the age-dependent COVID-19 infection fatality ratio from incomplete data. PLoS ONE. 16(2). e0246831–e0246831. 6 indexed citations
12.
Seoane, Beatriz & Alessandra Carbone. (2021). The complexity of protein interactions unravelled from structural disorder. PLoS Computational Biology. 17(1). e1008546–e1008546. 15 indexed citations
13.
Decelle, Aurélien, V. Martı́n-Mayor, & Beatriz Seoane. (2019). Learning a local symmetry with neural networks. Physical review. E. 100(5). 50102–50102. 12 indexed citations
14.
Cammarota, Chiara & Beatriz Seoane. (2016). First-principles computation of random-pinning glass transition, glass cooperative length scales, and numerical comparisons. Physical review. B.. 94(18). 3 indexed citations
15.
Charbonneau, Patrick, Yuliang Jin, Giorgio Parisi, et al.. (2015). Numerical detection of the Gardner transition in a mean-field glass former. Physical Review E. 92(1). 12316–12316. 35 indexed citations
16.
Parisi, Giorgio & Beatriz Seoane. (2014). Liquid-glass transition in equilibrium. Physical Review E. 89(2). 22309–22309. 20 indexed citations
17.
Fernández, L. A., V. Martı́n-Mayor, Beatriz Seoane, & P. Verrocchio. (2012). Equilibrium Fluid-Solid Coexistence of Hard Spheres. Physical Review Letters. 108(16). 165701–165701. 56 indexed citations
18.
Seoane, Beatriz & Hidetoshi Nishimori. (2012). Many-body transverse interactions in the quantum annealing of thep-spin ferromagnet. Journal of Physics A Mathematical and Theoretical. 45(43). 435301–435301. 45 indexed citations
19.
Fernández, L. A., V. Martı́n-Mayor, Beatriz Seoane, & P. Verrocchio. (2010). Separation and fractionation of order and disorder in highly polydisperse systems. Physical Review E. 82(2). 21501–21501. 6 indexed citations
20.
Fernández, L. A., V. Martı́n-Mayor, Giorgio Parisi, & Beatriz Seoane. (2010). Spin glasses on the hypercube. Physical Review B. 81(13). 6 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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