Pietro Faccioli

2.4k total citations
82 papers, 1.4k citations indexed

About

Pietro Faccioli is a scholar working on Molecular Biology, Atomic and Molecular Physics, and Optics and Nuclear and High Energy Physics. According to data from OpenAlex, Pietro Faccioli has authored 82 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Molecular Biology, 29 papers in Atomic and Molecular Physics, and Optics and 25 papers in Nuclear and High Energy Physics. Recurrent topics in Pietro Faccioli's work include Protein Structure and Dynamics (26 papers), Quantum Chromodynamics and Particle Interactions (24 papers) and High-Energy Particle Collisions Research (21 papers). Pietro Faccioli is often cited by papers focused on Protein Structure and Dynamics (26 papers), Quantum Chromodynamics and Particle Interactions (24 papers) and High-Energy Particle Collisions Research (21 papers). Pietro Faccioli collaborates with scholars based in Italy, United States and France. Pietro Faccioli's co-authors include S. a Beccara, Henri Orland, Tatjana Škrbić, Francesco Pederiva, Cristian Micheletti, Edward Shuryak, Marcello Sega, Roberto Covino, Giovanni Garberoglio and M. Traini and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Physical Review Letters.

In The Last Decade

Pietro Faccioli

77 papers receiving 1.3k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Pietro Faccioli 735 361 350 267 132 82 1.4k
Walter Nadler 644 0.9× 144 0.4× 612 1.7× 399 1.5× 308 2.3× 61 1.9k
Liao Y. Chen 431 0.6× 71 0.2× 790 2.3× 186 0.7× 242 1.8× 99 1.5k
Anders Irbäck 1.5k 2.0× 374 1.0× 414 1.2× 749 2.8× 77 0.6× 75 2.3k
Piotr Sułkowski 633 0.9× 181 0.5× 208 0.6× 189 0.7× 110 0.8× 41 1.1k
Jan O. Daldrop 253 0.3× 162 0.4× 285 0.8× 113 0.4× 191 1.4× 24 691
Andrew Ilin 600 0.8× 167 0.5× 244 0.7× 180 0.7× 24 0.2× 52 1.3k
Jemal Guven 454 0.6× 471 1.3× 359 1.0× 88 0.3× 244 1.8× 59 1.4k
Tetsuo Deguchi 157 0.2× 114 0.3× 573 1.6× 196 0.7× 477 3.6× 115 1.8k
Jian‐Min Yuan 358 0.5× 81 0.2× 1.2k 3.3× 176 0.7× 583 4.4× 109 2.0k
Kenneth C. Millett 820 1.1× 46 0.1× 271 0.8× 262 1.0× 98 0.7× 70 2.5k

Countries citing papers authored by Pietro Faccioli

Since Specialization
Citations

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

Fields of papers citing papers by Pietro Faccioli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pietro Faccioli

This figure shows the co-authorship network connecting the top 25 collaborators of Pietro Faccioli. A scholar is included among the top collaborators of Pietro Faccioli 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 Pietro Faccioli. Pietro Faccioli 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.
Spagnolli, Giovanni, Emanuela Zuccaro, Isabella Palazzolo, et al.. (2025). The evolution of eukaryotic linear motifs governing the function of androgen receptor from fish to Homo sapiens. Nucleic Acids Research. 53(14).
2.
Lolli, Graziano, Maria Pennuto, Jesús R. Requena, et al.. (2025). Mapping cryptic phosphorylation sites in the human proteome. The EMBO Journal. 44(22). 6704–6731.
3.
Hauke, Philipp, et al.. (2024). Protein Design by Integrating Machine Learning and Quantum-Encoded Optimization. Iris (University of Trento). 2(4). 2 indexed citations
4.
Micheletti, Cristian, et al.. (2023). RNA folding pathways from all-atom simulations with a variationally improved history-dependent bias. Biophysical Journal. 122(15). 3089–3098. 3 indexed citations
5.
Hauke, Philipp, et al.. (2023). Quantum-inspired encoding enhances stochastic sampling of soft matter systems. Science Advances. 9(43). eadi0204–eadi0204. 9 indexed citations
6.
Biasini, Emiliano & Pietro Faccioli. (2023). Functional, pathogenic, and pharmacological roles of protein folding intermediates. Proteins Structure Function and Bioinformatics. 93(8). 1299–1307. 6 indexed citations
7.
Roggero, Alessandro, et al.. (2022). Stochastic dynamics and bound states of heavy impurities in a Fermi bath. INO Open Portal.
8.
Spagnolli, Giovanni, et al.. (2022). Long-range allostery mediates the regulation of plasminogen activator inhibitor-1 by cell adhesion factor vitronectin. Journal of Biological Chemistry. 298(12). 102652–102652. 6 indexed citations
9.
Hauke, Philipp, et al.. (2021). Dominant Reaction Pathways by Quantum Computing. Physical Review Letters. 126(2). 28104–28104. 16 indexed citations
10.
Micheletti, Cristian, Philipp Hauke, & Pietro Faccioli. (2021). Polymer Physics by Quantum Computing. Physical Review Letters. 127(8). 80501–80501. 26 indexed citations
11.
Spagnolli, Giovanni, et al.. (2020). All-atom simulation of the HET-s prion replication. PLoS Computational Biology. 16(9). e1007922–e1007922. 9 indexed citations
12.
Spagnolli, Giovanni, et al.. (2019). Ok Google, how could I design therapeutics against prion diseases?. Current Opinion in Pharmacology. 44. 39–45. 4 indexed citations
13.
Gershenson, Anne, Shachi Gosavi, Pietro Faccioli, & Patrick L. Wintrode. (2019). Successes and challenges in simulating the folding of large proteins. Journal of Biological Chemistry. 295(1). 15–33. 55 indexed citations
14.
Wang, Fang, Simone Orioli, Alan Ianeselli, et al.. (2018). All-Atom Simulations Reveal How Single-Point Mutations Promote Serpin Misfolding. Biophysical Journal. 114(9). 2083–2094. 19 indexed citations
15.
Wang, Fang, et al.. (2015). Folding Mechanism of Proteins IM7 and IM9, from Computer Simulations in a Realistic Atomistic Force Field. Biophysical Journal. 108(2). 519a–519a. 1 indexed citations
16.
Beccara, S. a, Tatjana Škrbić, Roberto Covino, Cristian Micheletti, & Pietro Faccioli. (2013). Folding Pathways of a Knotted Protein with a Realistic Atomistic Force Field. PLoS Computational Biology. 9(3). e1003002–e1003002. 71 indexed citations
17.
Škrbić, Tatjana, Cristian Micheletti, & Pietro Faccioli. (2012). The Role of Non-Native Interactions in the Folding of Knotted Proteins. PLoS Computational Biology. 8(6). e1002504–e1002504. 51 indexed citations
18.
Corradini, Olindo, Pietro Faccioli, & Henri Orland. (2009). Simulating stochastic dynamics using large time steps. Physical Review E. 80(6). 61112–61112. 5 indexed citations
19.
Fogolari, Federico, Alessandra Corazza, Paolo Viglino, et al.. (2006). Molecular Dynamics Simulation Suggests Possible Interaction Patterns at Early Steps of β2-Microglobulin Aggregation. Biophysical Journal. 92(5). 1673–1681. 35 indexed citations
20.
Cristoforetti, M., Pietro Faccioli, Edward Shuryak, & M. Traini. (2004). Instantons, diquarks and the Delta I = 1/2 rule for non-leptonic hyperon decays. arXiv (Cornell University). 1 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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