Claudio Zeni

424 total citations
10 papers, 295 citations indexed

About

Claudio Zeni is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Claudio Zeni has authored 10 papers receiving a total of 295 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Materials Chemistry, 4 papers in Computational Theory and Mathematics and 2 papers in Molecular Biology. Recurrent topics in Claudio Zeni's work include Machine Learning in Materials Science (8 papers), Computational Drug Discovery Methods (4 papers) and Protein Structure and Dynamics (2 papers). Claudio Zeni is often cited by papers focused on Machine Learning in Materials Science (8 papers), Computational Drug Discovery Methods (4 papers) and Protein Structure and Dynamics (2 papers). Claudio Zeni collaborates with scholars based in Italy, Switzerland and United Kingdom. Claudio Zeni's co-authors include Aldo Glielmo, Alessandro De Vita, Kevin Rossi, Francesca Baletto, Stefano de Gironcoli, Richard E. Palmer, Joseph Kioseoglou, Alessandro Laio, Andrea Anelli and Bingqing Cheng and has published in prestigious journals such as Nature Communications, The Journal of Chemical Physics and Physical review. B..

In The Last Decade

Claudio Zeni

10 papers receiving 291 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Claudio Zeni Italy	7	224	89	59	39	32	10	295
Maria Carolina Muniz United States	7	243 1.1×	83 0.9×	73 1.2×	21 0.5×	90 2.8×	7	322
Jonathan Vandermause United States	7	285 1.3×	76 0.9×	64 1.1×	6 0.2×	38 1.2×	8	349
Jonas A. Finkler Switzerland	6	476 2.1×	174 2.0×	127 2.2×	16 0.4×	100 3.1×	11	524
Cas van der Oord United Kingdom	7	486 2.2×	152 1.7×	79 1.3×	6 0.2×	73 2.3×	7	527
Peize Lin China	11	281 1.3×	91 1.0×	52 0.9×	9 0.2×	183 5.7×	17	430
Viktor Zaverkin Germany	12	237 1.1×	120 1.3×	63 1.1×	15 0.4×	82 2.6×	14	313
Cory Hargus United States	6	182 0.8×	65 0.7×	56 0.9×	5 0.1×	82 2.6×	9	311
Aditi S. Krishnapriyan United States	8	174 0.8×	27 0.3×	21 0.4×	5 0.1×	42 1.3×	15	248
Joakim Brorsson Sweden	9	102 0.5×	14 0.2×	60 1.0×	8 0.2×	46 1.4×	18	240
Jack B. A. Davis United Kingdom	10	314 1.4×	30 0.3×	10 0.2×	114 2.9×	143 4.5×	12	406

Countries citing papers authored by Claudio Zeni

Since Specialization

Citations

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

Fields of papers citing papers by Claudio Zeni

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Claudio Zeni

This figure shows the co-authorship network connecting the top 25 collaborators of Claudio Zeni. A scholar is included among the top collaborators of Claudio Zeni 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 Claudio Zeni. Claudio Zeni 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

Rossi, Kevin, et al.. (2023). Modeling and characterization of the nucleation and growth of carbon nanostructures in physical synthesis. Carbon Trends. 11. 100268–100268. 5 indexed citations

Zeni, Claudio, Andrea Anelli, Aldo Glielmo, Stefano de Gironcoli, & Kevin Rossi. (2023). Divide-and-conquer potentials enable scalable and accurate predictions of forces and energies in atomistic systems. Digital Discovery. 3(1). 113–121. 6 indexed citations

Glielmo, Aldo, et al.. (2022). DADApy: Distance-based analysis of data-manifolds in Python. Patterns. 3(10). 100589–100589. 23 indexed citations

Glielmo, Aldo, Claudio Zeni, Bingqing Cheng, Gábor Cśanyi, & Alessandro Laio. (2022). Ranking the information content of distance measures. PNAS Nexus. 1(2). pgac039–pgac039. 27 indexed citations

Rossi, Kevin, et al.. (2022). Structural characterisation of nanoalloys for (photo)catalytic applications with the Sapphire library. Faraday Discussions. 242(0). 326–352. 4 indexed citations

Zeni, Claudio, Andrea Anelli, Aldo Glielmo, & Kevin Rossi. (2022). Exploring the robust extrapolation of high-dimensional machine learning potentials. Physical review. B.. 105(16). 25 indexed citations

Zeni, Claudio, Kevin Rossi, Joseph Kioseoglou, et al.. (2021). Data-driven simulation and characterisation of gold nanoparticle melting. Nature Communications. 12(1). 6056–6056. 53 indexed citations

Zeni, Claudio, Kevin Rossi, Aldo Glielmo, & Stefano de Gironcoli. (2021). Compact atomic descriptors enable accurate predictions via linear models. The Journal of Chemical Physics. 154(22). 224112–224112. 19 indexed citations

Zeni, Claudio, Kevin Rossi, Aldo Glielmo, & Francesca Baletto. (2019). On machine learning force fields for metallic nanoparticles. Advances in Physics X. 4(1). 1654919–1654919. 30 indexed citations

10.

Glielmo, Aldo, Claudio Zeni, & Alessandro De Vita. (2018). Efficient nonparametricn-body force fields from machine learning. Physical review. B.. 97(18). 103 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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