Jörg Behler
Impact in
- Materials Chemistry top 0.05%
- Machine Learning in Materials Science
- Phase-change materials and chalcogenides
- Catalytic Processes in Materials Science
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- Advanced Chemical Physics Studies
- Spectroscopy and Quantum Chemical Studies
Papers in
-
- Machine Learning in Materials Science 84
- Phase-change materials and chalcogenides 11
- Material Dynamics and Properties 6
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- Advanced Chemical Physics Studies 55
- Spectroscopy and Quantum Chemical Studies 16
- Co-authors
- Michele Parrinello (8 shared papers)Tobias Morawietz (9 shared papers)Nongnuch Artrith (4 shared papers)Christoph Dellago A (7 shared papers)Andreas Singraber (5 shared papers)Karsten Reuter (9 shared papers)Matti Hellström (15 shared papers)Tsz Wai Ko (5 shared papers)
- Journals
- The Journal of Chemical Physics (24 papers)Physical Chemistry Chemical Physics (14 papers)Physical Review B (10 papers)The Journal of Physical Chemistry C (10 papers)The Journal of Physical Chemistry Letters (9 papers)
- Partner nations
- GermanySwitzerlandUnited States
In The Last Decade
Jörg Behler
144 papers receiving 20.5k citations
Jörg Behler's Hit Papers
Peers
Comparison fields: 5 of 104
- Materials Chemistry 15.2k
- Atomic and Molecular Physics, and Optics 5.9k
- Physical and Theoretical Chemistry 1.4k
- Catalysis 172
- Atmospheric Science 442
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 148 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 | 4009 |
| 2 | Atom-centered symmetry functions for constructing high-dimensional neural network potentials Hit paper breakdown → | 2011 | 1348 |
| 3 | Perspective: Machine learning potentials for atomistic simulations Hit paper breakdown → | 2016 | 1203 |
| 4 | Performance and Cost Assessment of Machine Learning Interatomic Potentials Hit paper breakdown → | 2020 | 728 |
| 5 | Constructing high‐dimensional neural network potentials: A tutorial review Hit paper breakdown → | 2015 | 708 |
| 6 | Four Generations of High-Dimensional Neural Network Potentials Hit paper breakdown → | 2021 | 673 |
| 7 | Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations Hit paper breakdown → | 2011 | 649 |
| 8 | First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems Hit paper breakdown → | 2017 | 625 |
| 9 | Machine learning molecular dynamics for the simulation of infrared spectra Hit paper breakdown → | 2017 | 431 |
| 10 | A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer Hit paper breakdown → | 2021 | 410 |
| 11 | How van der Waals interactions determine the unique properties of water Hit paper breakdown → | 2016 | 408 |
| 12 | 2014 | 347 | |
| 13 | 2011 | 345 | |
| 14 | 2011 | 313 | |
| 15 | Ab initio thermodynamics of liquid and solid water Hit paper breakdown → | 2019 | 294 |
| 16 | 2012 | 292 | |
| 17 | 2005 | 270 | |
| 18 | 2018 | 268 | |
| 19 | 2019 | 258 | |
| 20 | Neural Network Potentials: A Concise Overview of Methods Hit paper breakdown → | 2022 | 242 |
About Jörg Behler
Jörg Behler is a scholar working on Materials Chemistry, Atomic and Molecular Physics, and Optics, Physical and Theoretical Chemistry, Atmospheric Science and Geophysics, having authored 148 papers that have together received 20.8k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (84 papers), Advanced Chemical Physics Studies (55 papers), Spectroscopy and Quantum Chemical Studies (16 papers), Phase-change materials and chalcogenides (11 papers), Advanced Physical and Chemical Molecular Interactions (9 papers), nanoparticles nucleation surface interactions (6 papers), Surface Chemistry and Catalysis (6 papers) and Material Dynamics and Properties (6 papers). The work is most often cited by research in Materials Chemistry (15.2k citations), Atomic and Molecular Physics, and Optics (5.9k citations), Physical and Theoretical Chemistry (1.4k citations), Catalysis (172 citations) and Atmospheric Science (442 citations). Jörg Behler has collaborated with scholars based in Germany, Switzerland and United States. Frequent co-authors include Michele Parrinello, Tobias Morawietz, Nongnuch Artrith, Christoph Dellago A, Andreas Singraber, Karsten Reuter, Matti Hellström, Tsz Wai Ko, Michele Ceriotti and Gábor Cśanyi. Their work appears in journals such as The Journal of Chemical Physics, Physical Chemistry Chemical Physics, Physical Review B, The Journal of Physical Chemistry C and The Journal of Physical Chemistry 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.