Attila Cangi

1.2k total citations
45 papers, 776 citations indexed

About

Attila Cangi is a scholar working on Atomic and Molecular Physics, and Optics, Materials Chemistry and Geophysics. According to data from OpenAlex, Attila Cangi has authored 45 papers receiving a total of 776 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Atomic and Molecular Physics, and Optics, 18 papers in Materials Chemistry and 15 papers in Geophysics. Recurrent topics in Attila Cangi's work include Advanced Chemical Physics Studies (19 papers), Machine Learning in Materials Science (16 papers) and High-pressure geophysics and materials (15 papers). Attila Cangi is often cited by papers focused on Advanced Chemical Physics Studies (19 papers), Machine Learning in Materials Science (16 papers) and High-pressure geophysics and materials (15 papers). Attila Cangi collaborates with scholars based in Germany, United States and India. Attila Cangi's co-authors include Tobias Dornheim, Jan Vorberger, Kieron Burke, Zhandos A. Moldabekov, Peter Elliott, Donghyung Lee, Kushal Ramakrishna, E. K. U. Gross, Maximilian Böhme and Michael Bußmann and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and The Journal of Chemical Physics.

In The Last Decade

Attila Cangi

44 papers receiving 759 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Attila Cangi Germany 17 532 279 208 127 70 45 776
Pablo López Ríos United Kingdom 17 908 1.7× 453 1.6× 193 0.9× 296 2.3× 108 1.5× 34 1.2k
Fionn D. Malone United States 15 618 1.2× 135 0.5× 143 0.7× 229 1.8× 25 0.4× 20 708
Stefano Pittalis Italy 25 919 1.7× 242 0.9× 53 0.3× 294 2.3× 191 2.7× 68 1.2k
Vladimir M. Bedanov Russia 12 604 1.1× 232 0.8× 96 0.5× 222 1.7× 44 0.6× 20 864
V. V. Khmelenko United States 20 1.2k 2.2× 108 0.4× 111 0.5× 73 0.6× 32 0.5× 104 1.3k
Nils Erik Dahlen Netherlands 13 760 1.4× 155 0.6× 24 0.1× 178 1.4× 209 3.0× 16 843
Nobuyuki Ōkuma Japan 15 1.6k 3.1× 547 2.0× 20 0.1× 136 1.1× 79 1.1× 27 2.2k
Stefan Wehinger United States 4 605 1.1× 133 0.5× 49 0.2× 136 1.1× 116 1.7× 7 805
Klaus Morawetz Germany 17 743 1.4× 92 0.3× 75 0.4× 222 1.7× 136 1.9× 118 1.0k
Makoto Kaburagi Japan 20 690 1.3× 263 0.9× 41 0.2× 997 7.9× 60 0.9× 94 1.4k

Countries citing papers authored by Attila Cangi

Since Specialization
Citations

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

Fields of papers citing papers by Attila Cangi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Attila Cangi

This figure shows the co-authorship network connecting the top 25 collaborators of Attila Cangi. A scholar is included among the top collaborators of Attila Cangi 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 Attila Cangi. Attila Cangi 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.
Cangi, Attila, Andrew Rohskopf, Dayton J. Vogel, et al.. (2025). Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations. Computer Physics Communications. 314. 109654–109654. 1 indexed citations
2.
Cangi, Attila. (2024). Bridging the gap in electronic structure calculations via machine learning. Nature Computational Science. 4(10). 729–730. 1 indexed citations
3.
Cangi, Attila, et al.. (2024). Inverting the Kohn–Sham equations with physics-informed machine learning. Machine Learning Science and Technology. 5(1). 15050–15050. 2 indexed citations
4.
Ramakrishna, Kushal, et al.. (2024). Machine learning-driven structure prediction for iron hydrides. Physical Review Materials. 8(3). 1 indexed citations
5.
Moldabekov, Zhandos A., et al.. (2023). Assessing the accuracy of hybrid exchange-correlation functionals for the density response of warm dense electrons. The Journal of Chemical Physics. 158(9). 94105–94105. 19 indexed citations
6.
Dornheim, Tobias, Maximilian Böhme, D. A. Chapman, et al.. (2023). Imaginary-time correlation function thermometry: A new, high-accuracy and model-free temperature analysis technique for x-ray Thomson scattering data. Physics of Plasmas. 30(4). 29 indexed citations
7.
Ramakrishna, Kushal, et al.. (2023). Transferable interatomic potential for aluminum from ambient conditions to warm dense matter. Physical Review Research. 5(3). 7 indexed citations
8.
Ramakrishna, Kushal, et al.. (2023). Impact of electronic correlations on high-pressure iron: insights from time-dependent density functional theory. Electronic Structure. 5(4). 45002–45002. 3 indexed citations
9.
Ramakrishna, Kushal, Attila Cangi, D. Murali, et al.. (2023). Ab initio insights on the ultrafast strong-field dynamics of anatase TiO2. Physical review. B.. 108(19). 1 indexed citations
10.
Modine, Normand A., et al.. (2023). Predicting electronic structures at any length scale with machine learning. npj Computational Materials. 9(1). 40 indexed citations
11.
Kraisler, Eli, et al.. (2023). Improved calculations of mean ionization states with an average-atom model. Physical Review Research. 5(1). 10 indexed citations
12.
Schörner, Maximilian, B. B. L. Witte, Andrew Baczewski, Attila Cangi, & R. Redmer. (2022). Ab initio study of shock-compressed copper. Physical review. B.. 106(5). 14 indexed citations
13.
Moldabekov, Zhandos A., Tobias Dornheim, G. Gregori, et al.. (2022). Towards a quantum fluid theory of correlated many-fermion systems from first principles. SciPost Physics. 12(2). 16 indexed citations
14.
Ellis, John, et al.. (2022). Training-free hyperparameter optimization of neural networks for electronic structures in matter. Machine Learning Science and Technology. 3(4). 45008–45008. 6 indexed citations
15.
Moldabekov, Zhandos A., et al.. (2022). Accelerating equilibration in first-principles molecular dynamics with orbital-free density functional theory. Physical Review Research. 4(4). 20 indexed citations
16.
Moldabekov, Zhandos A., Tobias Dornheim, Jan Vorberger, & Attila Cangi. (2022). Benchmarking exchange-correlation functionals in the spin-polarized inhomogeneous electron gas under warm dense conditions. Physical review. B.. 105(3). 24 indexed citations
17.
Bußmann, Michael, et al.. (2022). Deep dive into machine learning density functional theory for materials science and chemistry. Physical Review Materials. 6(4). 61 indexed citations
18.
Moldabekov, Zhandos A., et al.. (2021). Higher harmonics in complex plasmas with alternating screening. Physical Review Research. 3(4). 4 indexed citations
19.
Ramakrishna, Kushal, Attila Cangi, Tobias Dornheim, Andrew Baczewski, & Jan Vorberger. (2021). First-principles modeling of plasmons in aluminum under ambient and extreme conditions. Physical review. B.. 103(12). 28 indexed citations
20.
Dornheim, Tobias, Attila Cangi, Kushal Ramakrishna, et al.. (2020). Effective Static Approximation: A Fast and Reliable Tool for Warm-Dense Matter Theory. Physical Review Letters. 125(23). 235001–235001. 60 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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