Gian‐Marco Rignanese

21.1k total citations · 4 hit papers
168 papers, 11.3k citations indexed

About

Gian‐Marco Rignanese is a scholar working on Materials Chemistry, Electrical and Electronic Engineering and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Gian‐Marco Rignanese has authored 168 papers receiving a total of 11.3k indexed citations (citations by other indexed papers that have themselves been cited), including 108 papers in Materials Chemistry, 70 papers in Electrical and Electronic Engineering and 43 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Gian‐Marco Rignanese's work include Machine Learning in Materials Science (30 papers), Electronic and Structural Properties of Oxides (28 papers) and Semiconductor materials and devices (25 papers). Gian‐Marco Rignanese is often cited by papers focused on Machine Learning in Materials Science (30 papers), Electronic and Structural Properties of Oxides (28 papers) and Semiconductor materials and devices (25 papers). Gian‐Marco Rignanese collaborates with scholars based in Belgium, United States and China. Gian‐Marco Rignanese's co-authors include Xavier Gonze, Geoffroy Hautier, Matthieu J. Verstraete, Matteo Giantomassi, Jean‐Christophe Charlier, Philippe Ghosez, Michiel J. van Setten, Alfredo Pasquarello, F. Detraux and D. R. Hamann and has published in prestigious journals such as Journal of the American Chemical Society, Physical Review Letters and Angewandte Chemie International Edition.

In The Last Decade

Gian‐Marco Rignanese

160 papers receiving 11.0k citations

Hit Papers

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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.

First-principles computation of material properties: the ABINIT software project

2002 2.6k citations Xavier Gonze, Gian‐Marco Rignanese et al. profile →
The PseudoDojo: Training and grading a 85 element optimized norm-conserving pseudopotential table

2018 1.4k citations Xavier Gonze, Gian‐Marco Rignanese et al. profile →
Low Hole Effective Mass p-type Transparent Conducting Oxides: Identification and Design Principles

2013 520 citations Geoffroy Hautier, Anna Miglio et al. profile →
FireWorks: a dynamic workflow system designed for high‐throughput applications

2015 393 citations Anubhav Jain, Shyue Ping Ong et al. Concurrency and Computation Practice and Experience profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gian‐Marco Rignanese Belgium	50	8.2k	4.3k	2.5k	2.2k	1.2k	168	11.3k
Ji Feng China	46	8.1k 1.0×	4.2k 1.0×	2.4k 1.0×	2.1k 1.0×	910 0.8×	153	10.4k
Lucian A. Constantin Italy	34	8.5k 1.0×	3.6k 0.8×	3.9k 1.6×	3.2k 1.5×	1.7k 1.4×	90	12.5k
E. A. Kotomin Latvia	53	9.1k 1.1×	3.4k 0.8×	1.5k 0.6×	3.4k 1.6×	1.3k 1.1×	451	11.4k
Claudia Draxl Austria	59	8.9k 1.1×	5.6k 1.3×	4.2k 1.7×	3.4k 1.5×	1.8k 1.5×	310	14.5k
Vidvuds Ozoliņš United States	60	9.1k 1.1×	4.3k 1.0×	2.0k 0.8×	2.6k 1.2×	1.3k 1.1×	146	13.0k
Alexander L. Shluger United Kingdom	60	8.0k 1.0×	7.2k 1.7×	3.7k 1.5×	1.2k 0.5×	703 0.6×	349	14.5k
Jianwei Sun United States	45	7.5k 0.9×	3.6k 0.8×	3.8k 1.5×	2.2k 1.0×	1.3k 1.1×	142	12.2k
Matteo Cococcioni United States	35	5.4k 0.7×	3.4k 0.8×	1.6k 0.7×	2.5k 1.1×	1.4k 1.2×	64	9.4k
Fumiyasu Oba Japan	54	12.4k 1.5×	5.4k 1.3×	1.7k 0.7×	3.8k 1.7×	1.6k 1.3×	223	15.3k
Joachim Paier Germany	34	7.8k 1.0×	3.0k 0.7×	2.5k 1.0×	1.6k 0.7×	995 0.8×	69	10.2k

Countries citing papers authored by Gian‐Marco Rignanese

Since Specialization

Citations

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

Fields of papers citing papers by Gian‐Marco Rignanese

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gian‐Marco Rignanese. 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 Gian‐Marco Rignanese. The network helps show where Gian‐Marco Rignanese may publish in the future.

Co-authorship network of co-authors of Gian‐Marco Rignanese

This figure shows the co-authorship network connecting the top 25 collaborators of Gian‐Marco Rignanese. A scholar is included among the top collaborators of Gian‐Marco Rignanese 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 Gian‐Marco Rignanese. Gian‐Marco Rignanese 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

Brunin, Guillaume, et al.. (2025). Author Correction: Phonon-limited mobility for electrons and holes in highly-strained silicon. npj Computational Materials. 11(1).

Wang, Weihua & Gian‐Marco Rignanese. (2025). AI for Science―a journal to serve the AI-driven scientific innovation. SHILAP Revista de lepidopterología. 1(1). 10201–10201.

Prabhakar, Rajiv Ramanujam, Sudhanshu Shukla, Haoyi Li, et al.. (2025). Origin of photoelectrochemical CO₂ reduction on bare Cu(In,Ga)S₂ (CIGS) thin films in aqueous media without co-catalysts. EES Catalysis. 3(2). 327–336. 1 indexed citations

Quintana, Alberto, Gemma Rius, Aliona Nicolenco, et al.. (2025). Composition-Dependent Voltage-Driven OFF-ON Switching of Ferromagnetism in Co–Ni Oxide Microdisks. ACS Applied Materials & Interfaces. 17(6). 9500–9513.

Evans, Matthew L., et al.. (2025). Accelerating the discovery of high-performance nonlinear optical materials using active learning and high-throughput screening. Journal of Materials Chemistry C. 13(35). 18197–18212.

Gorsse, Stéphane, Wei-Chih Lin, Hideyuki Murakami, Gian‐Marco Rignanese, & An‐Chou Yeh. (2024). Advancing refractory high entropy alloy development with AI-predictive models for high temperature oxidation resistance. Scripta Materialia. 255. 116394–116394. 10 indexed citations

Naccarato, Francesco, Guillaume Brunin, Guido Petretto, et al.. (2024). Second-harmonic generation tensors from high-throughput density-functional perturbation theory. Scientific Data. 11(1). 757–757. 9 indexed citations

Lee, Shannon, Andrew P. Porter, Gayatri Viswanathan, et al.. (2024). FeSi₄P₄ and CoSi₃P₃: Hidden Gems of Ternary Tetrel Pnictides with Outstanding Nonlinear Optical Properties. Chemistry of Materials. 5 indexed citations

Roach, Lucien, Gian‐Marco Rignanese, Arnaud Erriguible, & Cyril Aymonier. (2023). Applications of machine learning in supercritical fluids research. The Journal of Supercritical Fluids. 202. 106051–106051. 27 indexed citations

10.

Brunin, Guillaume, Ke Wang, Rui Zu, et al.. (2023). MgSiP₂: An Infrared Nonlinear Optical Crystal with a Large Non‐Resonant Phase‐Matchable Second Harmonic Coefficient and High Laser Damage Threshold. Advanced Optical Materials. 11(24). 9 indexed citations

11.

Teulé‐Gay, Lionel, et al.. (2022). Toward the Prediction of Electrochromic Properties of WO₃ Films: Combination of Experimental and Machine Learning Approaches. The Journal of Physical Chemistry Letters. 13(34). 8111–8115. 17 indexed citations

12.

Wang, Linghui, et al.. (2022). A simple denoising approach to exploit multi-fidelity data for machine learning materials properties. npj Computational Materials. 8(1). 10 indexed citations

13.

Shukla, Sudhanshu, Mohit Sood, Gunnar Kusch, et al.. (2021). Over 15% efficient wide-band-gap Cu(In,Ga)S2 solar cell: Suppressing bulk and interface recombination through composition engineering. Joule. 5(7). 1816–1831. 55 indexed citations

14.

Perez, Christopher J., Max Wood, Francesco Ricci, et al.. (2021). Discovery of multivalley Fermi surface responsible for the high thermoelectric performance in Yb ₁₄ MnSb ₁₁ and Yb ₁₄ MgSb ₁₁. Science Advances. 7(4). 49 indexed citations

15.

Brunin, Guillaume, Francesco Ricci, Viet-Anh Ha, Gian‐Marco Rignanese, & Geoffroy Hautier. (2019). Transparent conducting materials discovery using high-throughput computing. npj Computational Materials. 5(1). 133 indexed citations

16.

Rignanese, Gian‐Marco. (2016). The ABINIT software project. Bulletin of the American Physical Society. 2016. 2 indexed citations

17.

Jain, Anubhav, Shyue Ping Ong, Wei Chen, et al.. (2015). FireWorks: a dynamic workflow system designed for high‐throughput applications. Concurrency and Computation Practice and Experience. 27(17). 5037–5059. 393 indexed citations breakdown →

18.

Hautier, Geoffroy, Anna Miglio, Gerbrand Ceder, Gian‐Marco Rignanese, & Xavier Gonze. (2013). Identification and design principles of low hole effective mass p-type transparent conducting oxides. Nature Communications. 4(1). 2292–2292. 2 indexed citations

19.

Rignanese, Gian‐Marco, Jean‐Christophe Charlier, & Xavier Gonze. (2003). First-principles study of vibrational and dielectric properties of C_3N 4 polymorphs. APS March Meeting Abstracts. 2003. 1 indexed citations

20.

Li, Je-Luen, Gian‐Marco Rignanese, & Steven G. Louie. (2000). Quasiparticle Energy Bands of NiO in the GW approximation. APS March Meeting Abstracts. 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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