Jörg Behler
Impact in
- Materials Chemistry top 0.1%
- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Electronic and Structural Properties of Oxides
- Computational Theory and Mathematics top 0.05%
- Computational Drug Discovery Methods
Papers in
-
- Machine Learning in Materials Science 72
- X-ray Diffraction in Crystallography 14
- Phase-change materials and chalcogenides 13
- Electronic and Structural Properties of Oxides 12
-
- Advanced Chemical Physics Studies 29
- Spectroscopy and Quantum Chemical Studies 26
- Quantum, superfluid, helium dynamics 15
- Co-authors
- Michele Parrinello (7 shared papers)Nongnuch Artrith (4 shared papers)Christoph Dellago (7 shared papers)Tobias Morawietz (7 shared papers)Andreas Singraber (5 shared papers)Matti Hellström (11 shared papers)Gábor Cśanyi (3 shared papers)Marco Bernasconi (13 shared papers)
- Journals
- The Journal of Chemical Physics (17 papers)Physical Chemistry Chemical Physics (13 papers)The Journal of Physical Chemistry C (10 papers)Physical Review B (10 papers)Physical Review Letters (9 papers)
- Partner nations
- GermanySwitzerlandItaly
In The Last Decade
Jörg Behler
118 papers receiving 16.1k citations
Jörg Behler's Hit Papers
Peers
Comparison fields: 5 of 112
- Materials Chemistry 13.4k
- Computational Theory and Mathematics 3.5k
- Catalysis 1.0k
- Atomic and Molecular Physics, and Optics 4.3k
- Structural Biology 190
Countries citing papers authored by Jörg Behler
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
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-authors
The 25 scholars most cited alongside Jörg Behler, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 122 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces Hit paper breakdown → | 2007 | 3490 |
| 2 | Atom-centered symmetry functions for constructing high-dimensional neural network potentials Hit paper breakdown → | 2011 | 1176 |
| 3 | Perspective: Machine learning potentials for atomistic simulations Hit paper breakdown → | 2016 | 1022 |
| 4 | Performance and Cost Assessment of Machine Learning Interatomic Potentials Hit paper breakdown → | 2020 | 643 |
| 5 | Constructing high‐dimensional neural network potentials: A tutorial review Hit paper breakdown → | 2015 | 638 |
| 6 | Four Generations of High-Dimensional Neural Network Potentials Hit paper breakdown → | 2021 | 586 |
| 7 | Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations Hit paper breakdown → | 2011 | 581 |
| 8 | First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems Hit paper breakdown → | 2017 | 533 |
| 9 | A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer Hit paper breakdown → | 2021 | 364 |
| 10 | How van der Waals interactions determine the unique properties of water Hit paper breakdown → | 2016 | 330 |
| 11 | 2014 | 315 | |
| 12 | 2011 | 299 | |
| 13 | Ab initio thermodynamics of liquid and solid water Hit paper breakdown → | 2019 | 260 |
| 14 | 2012 | 258 | |
| 15 | 2005 | 251 | |
| 16 | 2019 | 223 | |
| 17 | 2012 | 191 | |
| 18 | 2008 | 184 | |
| 19 | 2021 | 166 | |
| 20 | 2019 | 165 |
About Jörg Behler
Jörg Behler is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering, Computational Theory and Mathematics and Biomedical Engineering, having authored 122 papers that have together received 16.2k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (72 papers), Advanced Chemical Physics Studies (29 papers), Spectroscopy and Quantum Chemical Studies (26 papers), Computational Drug Discovery Methods (16 papers), Quantum, superfluid, helium dynamics (15 papers), X-ray Diffraction in Crystallography (14 papers), Phase-change materials and chalcogenides (13 papers) and Electronic and Structural Properties of Oxides (12 papers). The work is most often cited by research in Materials Chemistry (13.4k citations), Computational Theory and Mathematics (3.5k citations), Catalysis (1.0k citations), Atomic and Molecular Physics, and Optics (4.3k citations) and Structural Biology (190 citations). Jörg Behler has collaborated with scholars based in Germany, Switzerland and Italy. Frequent co-authors include Michele Parrinello, Nongnuch Artrith, Christoph Dellago, Tobias Morawietz, Andreas Singraber, Matti Hellström, Gábor Cśanyi, Marco Bernasconi, Karsten Reuter and Gabriele C. Sosso. Their work appears in journals such as The Journal of Chemical Physics, Physical Chemistry Chemical Physics, The Journal of Physical Chemistry C, Physical Review B and Physical Review Letters.
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.