Jörg Behler

22.0k total citations · 11 hit papers
121 papers, 15.7k citations indexed

About

Jörg Behler is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics and Electrical and Electronic Engineering. According to data from OpenAlex, Jörg Behler has authored 121 papers receiving a total of 15.7k indexed citations (citations by other indexed papers that have themselves been cited), including 95 papers in Materials Chemistry, 49 papers in Atomic and Molecular Physics, and Optics and 20 papers in Electrical and Electronic Engineering. Recurrent topics in Jörg Behler's work include Machine Learning in Materials Science (72 papers), Advanced Chemical Physics Studies (29 papers) and Spectroscopy and Quantum Chemical Studies (26 papers). Jörg Behler is often cited by papers focused on Machine Learning in Materials Science (72 papers), Advanced Chemical Physics Studies (29 papers) and Spectroscopy and Quantum Chemical Studies (26 papers). Jörg Behler collaborates with scholars based in Germany, Switzerland and Italy. Jörg Behler's co-authors include Michele Parrinello, Nongnuch Artrith, Christoph Dellago, Tobias Morawietz, Andreas Singraber, Matti Hellström, Marco Bernasconi, Gábor Cśanyi, Karsten Reuter and Gabriele C. Sosso and has published in prestigious journals such as Chemical Reviews, Proceedings of the National Academy of Sciences and Physical Review Letters.

In The Last Decade

Jörg Behler

117 papers receiving 15.5k citations

Hit Papers

Generalized Neural-Network Representation of High-Dimensi... 2007 2026 2013 2019 2007 2011 2016 2015 2020 1000 2.0k 3.0k
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.

  1. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
    2007 3.3k citations Jörg Behler, Michele Parrinello Physical Review Letters profile →
  2. Atom-centered symmetry functions for constructing high-dimensional neural network potentials
    2011 1.1k citations Jörg Behler The Journal of Chemical Physics profile →
  3. Perspective: Machine learning potentials for atomistic simulations
    2016 993 citations Jörg Behler The Journal of Chemical Physics profile →
  4. Constructing high‐dimensional neural network potentials: A tutorial review
    2015 622 citations Jörg Behler profile →
  5. Performance and Cost Assessment of Machine Learning Interatomic Potentials
    2020 617 citations Jörg Behler et al. profile →
  6. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
    2011 572 citations Jörg Behler profile →
  7. Four Generations of High-Dimensional Neural Network Potentials
    2021 545 citations Jörg Behler profile →
  8. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems
    2017 515 citations Jörg Behler profile →
  9. A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
    2021 339 citations Tsz Wai Ko, Jonas A. Finkler et al. Nature Communications profile →
  10. How van der Waals interactions determine the unique properties of water
    2016 321 citations Andreas Singraber, Christoph Dellago et al. profile →
  11. Ab initio thermodynamics of liquid and solid water
    2019 248 citations Jörg Behler, Christoph Dellago et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jörg Behler Germany 54 13.1k 4.2k 3.5k 2.6k 2.2k 121 15.7k
Gábor Cśanyi United Kingdom 64 14.8k 1.1× 3.2k 0.8× 3.7k 1.1× 3.2k 1.2× 2.1k 1.0× 175 18.3k
O. Anatole von Lilienfeld Switzerland 48 8.3k 0.6× 2.9k 0.7× 4.5k 1.3× 1.2k 0.5× 2.3k 1.0× 115 11.1k
Alexandre Tkatchenko Luxembourg 69 17.0k 1.3× 8.7k 2.0× 4.3k 1.2× 5.3k 2.0× 3.0k 1.4× 241 25.8k
Michele Ceriotti Switzerland 51 6.6k 0.5× 3.9k 0.9× 2.1k 0.6× 1.1k 0.4× 1.8k 0.8× 166 10.9k
Stefan Goedecker Switzerland 53 10.2k 0.8× 7.2k 1.7× 869 0.3× 4.3k 1.7× 919 0.4× 161 18.7k
Albert P. Bartók United Kingdom 24 7.0k 0.5× 1.6k 0.4× 2.1k 0.6× 1.2k 0.5× 1.2k 0.5× 50 8.4k
Miguel A. L. Marques Germany 58 8.6k 0.7× 6.6k 1.5× 530 0.2× 3.8k 1.4× 660 0.3× 250 16.5k
Volker L. Deringer United Kingdom 48 11.3k 0.9× 1.6k 0.4× 930 0.3× 4.8k 1.8× 472 0.2× 142 15.1k
Artem R. Oganov Russia 77 18.6k 1.4× 3.9k 0.9× 698 0.2× 3.1k 1.2× 365 0.2× 345 26.3k
Bobby G. Sumpter United States 77 14.3k 1.1× 3.6k 0.9× 462 0.1× 8.9k 3.4× 1.2k 0.5× 583 25.0k

Countries citing papers authored by Jörg Behler

Since Specialization
Citations

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

Fields of papers citing papers by Jörg Behler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jörg Behler

This figure shows the co-authorship network connecting the top 25 collaborators of Jörg Behler. A scholar is included among the top collaborators of Jörg Behler 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 Jörg Behler. Jörg Behler 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.
Behler, Jörg, et al.. (2025). Long-Range Interactions in High-Dimensional Neural Network Potentials: A Benchmark Study for Small Organic Molecules. The Journal of Physical Chemistry B. 129(48). 12518–12528.
2.
Saitta, A. Marco, et al.. (2025). Free energy profiles for chemical reactions in solution from high-dimensional neural network potentials: The case of the Strecker synthesis. The Journal of Chemical Physics. 162(17). 1 indexed citations
3.
Singraber, Andreas, et al.. (2025). Iterative charge equilibration for fourth-generation high-dimensional neural network potentials. The Journal of Chemical Physics. 162(12). 3 indexed citations
4.
5.
Menon, Sarath, Yury Lysogorskiy, Jan Janßen, et al.. (2024). From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows. npj Computational Materials. 10(1). 10 indexed citations
6.
Wada, Toru, et al.. (2023). Accelerating Non-Empirical Structure Determination of Ziegler–Natta Catalysts with a High-Dimensional Neural Network Potential. The Journal of Physical Chemistry C. 127(24). 11683–11691. 3 indexed citations
7.
Herbst‐Irmer, Regine, et al.. (2023). A new polymorph of white phosphorus at ambient conditions. IUCrJ. 10(6). 766–771. 3 indexed citations
8.
Behler, Jörg, et al.. (2023). High-Dimensional Neural Network Potentials for Accurate Prediction of Equation of State: A Case Study of Methane. Journal of Chemical Theory and Computation. 19(21). 7825–7832. 2 indexed citations
9.
Ko, Tsz Wai, Jonas A. Finkler, Stefan Goedecker, & Jörg Behler. (2021). A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer. Nature Communications. 12(1). 398–398. 339 indexed citations breakdown →
10.
Behler, Jörg, et al.. (2021). Insights into lithium manganese oxide–water interfaces using machine learning potentials. The Journal of Chemical Physics. 155(24). 244703–244703. 33 indexed citations
11.
12.
Blöchl, Peter E., et al.. (2020). Hybrid density functional theory benchmark study on lithium manganese oxides. Physical review. B.. 101(20). 19 indexed citations
13.
Keil, Helena, et al.. (2019). New Insights into the Catalytic Activity of Cobalt Orthophosphate Co3(PO4)2 from Charge Density Analysis. Chemistry - A European Journal. 25(69). 15786–15794. 7 indexed citations
14.
Behler, Jörg, et al.. (2019). From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5. Journal of Chemical Theory and Computation. 15(6). 3793–3809. 96 indexed citations
15.
Shakouri, Kh., et al.. (2019). Orbital-Dependent Electronic Friction Significantly Affects the Description of Reactive Scattering of N2 from Ru(0001). The Journal of Physical Chemistry Letters. 10(11). 2957–2962. 55 indexed citations
16.
Behler, Jörg. (2019). High-Dimensional Neural Network Potentials for Complex Systems. Bulletin of the American Physical Society. 2019.
17.
Campi, Davide, et al.. (2019). Atomistic simulations of thermal conductivity in GeTe nanowires. Journal of Physics D Applied Physics. 53(5). 54001–54001. 23 indexed citations
18.
Shakouri, Kh., Jörg Behler, Jörg Meyer, & Geert–Jan Kroes. (2018). Analysis of Energy Dissipation Channels in a Benchmark System of Activated Dissociation: N2 on Ru(0001). The Journal of Physical Chemistry C. 122(41). 23470–23480. 18 indexed citations
19.
Shakouri, Kh., Jörg Behler, Jörg Meyer, & Geert–Jan Kroes. (2017). Accurate Neural Network Description of Surface Phonons in Reactive Gas–Surface Dynamics: N2 + Ru(0001). The Journal of Physical Chemistry Letters. 8(10). 2131–2136. 129 indexed citations
20.
Behler, Jörg & Michele Parrinello. (2007). Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces. Physical Review Letters. 98(14). 146401–146401. 3341 indexed citations breakdown →

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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