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Gábor Csányi

Active 2003–2026

Also published as
Gábor Cśanyi
125
Papers
26,147
Citations
67
h-index
107
i10-index

Citations

Citations per year for Gábor Csányi1911: 2 citations1971: 1 citations1989: 2 citations1995: 1 citations2004: 5 citations2005: 9 citations2006: 9 citations2007: 18 citations2008: 30 citations2009: 37 citations2010: 30 citations2011: 50 citations2012: 48 citations2013: 64 citations2014: 79 citations2015: 91 citations2016: 106 citations2017: 177 citations2018: 323 citations2019: 547 citations2020: 877 citations2021: 819 citations2022: 809 citations2023: 809 citations2024: 1,080 citations2025: 918 citations2026: 182 citations1912–1970: no citations, so these years are not shown1972–1988: no citations, so these years are not shown1990–1994: no citations, so these years are not shown1996–2003: no citations, so these years are not shown

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 1,404 citing papers, 25.2% of this breakdownChina: 703 citing papers, 12.6% of this breakdownUnited Kingdom: 520 citing papers, 9.3% of this breakdownGermany: 476 citing papers, 8.5% of this breakdownSwitzerland: 294 citing papers, 5.3% of this breakdownFrance: 172 citing papers, 3.1% of this breakdownJapan: 165 citing papers, 3% of this breakdownItaly: 159 citing papers, 2.8% of this breakdownCanada: 148 citing papers, 2.7% of this breakdownSouth Korea: 117 citing papers, 2.1% of this breakdownSpain: 98 citing papers, 1.8% of this breakdownAustria: 92 citing papers, 1.6% of this breakdown
0%25.2%Other 22%

Institutions

Fields

  • Materials Science50.5%
  • Computer Science11%
  • Biochemistry, Genetics and Molecular Biology10%
  • Medicine8.8%
  • Immunology and Microbiology4.8%
  • Physics and Astronomy4.8%
  • Other10.1%

Topics

  • Machine Learning in Materials Science17.8%
  • Computational Drug Discovery Methods11.6%
  • Protein Structure and Dynamics5.7%
  • X-ray Diffraction in Crystallography2.4%
  • Advanced Chemical Physics Studies1.8%
  • Neutrophil, Myeloperoxidase and Oxidative Mechanisms1.4%
  • Other59.3%

Coauthors

All papers

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  1. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

    Authors: Albert P. Bartók, M. C. Payne, Risi Kondor, Gábor Cśanyi - Physical Review Letters 2010 cited by 3,143

  2. On representing chemical environments

    Authors: Albert P. Bartók, Risi Kondor, Gábor Cśanyi - Physical Review B 2013 cited by 2,639

  3. Gaussian Process Regression for Materials and Molecules

    Authors: Volker L. Deringer, Albert P. Bartók, Noam Bernstein, David M. Wilkins, Michele Ceriotti, Gábor Cśanyi - Chemical Reviews 2021 cited by 1,204

  4. A foundation model for atomistic materials chemistry

    Authors: Batatia, Ilyes, Philipp Benner, Yuan Chiang, Alin M. Elena, Dávid Péter Kovács, Janosh Riebesell, Xavier R. Advincula, Mark Asta, Avaylon, Matthew, William J. Baldwin, Berger, Fabian, Noam Bernstein, Arghya Bhowmik, Bigi, Filippo, Samuel M. Blau, Vlad Cărare, Ceriotti, Michele, Chong, Sanggyu, James P. Darby, Sandip De, Flaviano Della Pia, Volker L. Deringer, Rokas Elijošius, Zakariya El‐Machachi, Falcioni, Fabio, Edvin Fako, Andrea C. Ferrari, Gardner, John L. A., Gawkowski, Mikolaj J., Annalena R. Genreith‐Schriever, Janine George, Rhys E. A. Goodall, Grandel, Jonas, Clare P. Grey, Grigorev, Petr, Shuang Han, Will Handley, Hendrik H. Heenen, Kersti Hermansson, Christian Holm, Ho, Cheuk Hin, Stephan Hofmann, Jad Jaafar, Konstantin S. Jakob, Hyunwook Jung, Venkat Kapil, Aaron D. Kaplan, Nima Karimitari, Kermode, James R., Kourtis, Panagiotis, Namu Kroupa, Jolla Kullgren, Matthew C. Kuner, Domantas Kuryla, Guoda Liepuoniute, Lin, Chen, Johannes T. Margraf, Ioan-Bogdan Magdău, Angelos Michaelides, J. Harry Moore, Aakash Ashok Naik, Samuel P. Niblett, Sam Walton Norwood, Niamh O’Neill, Christoph Ortner, Kristin A. Persson, Karsten Reuter, Andrew Rosen, Rosset, Louise A. M., Lars L. Schaaf, Christoph Schran, Shi, Benjamin X., Eric Sivonxay, Tamás K. Stenczel, Viktor Svahn, Christopher Sutton, Swinburne, Thomas D., Tilly, Jules, Cas van der Oord, Vargas, Santiago, Eszter Varga-Umbrich, Tejs Vegge, Martin Vondrák, Yangshuai Wang, William C. Witt, Wolf, Thomas, Fabian Zills, Gábor Csányi - arXiv (Cornell University) 2023 cited by 246

  5. MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

    Authors: Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, Gábor Csányi - Advances in Neural Information Processing Systems 35, NeurIPS 2022 cited by 1,193

  6. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

    Authors: Volker L. Deringer, Miguel A. Caro, Gábor Cśanyi - Advanced Materials 2019 cited by 989

  7. Comparing molecules and solids across structural and alchemical space

    Authors: Sandip De, Albert P. Bartók, Gábor Cśanyi, Michele Ceriotti - Physical Chemistry Chemical Physics 2016 cited by 808

  8. Evaluation of the MACE force field architecture: From medicinal chemistry to materials science

    Authors: Dávid Péter Kovács, Ilyes Batatia, Eszter Sára Arany, Gábor Cśanyi - The Journal of Chemical Physics 2023 cited by 175

  9. Performance and Cost Assessment of Machine Learning Interatomic Potentials

    Authors: Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Cśanyi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell Wood, Shyue Ping Ong - The Journal of Physical Chemistry A 2020 cited by 927

  10. MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

    Authors: Dávid Péter Kovács, J. Harry Moore, Nicholas J. Browning, Ilyes Batatia, Joshua T. Horton, Pu, Yixuan, Venkat Kapil, Witt, William C., Ioan-Bogdan Magdău, Daniel J. Cole, Gábor Cśanyi - arXiv (Cornell University) 2023 cited by 66

  11. The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

    Authors: Ilyes Batatia, Simon L. Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi - arXiv (Cornell University), CoRR 2022 cited by 101

  12. Machine learning unifies the modeling of materials and molecules

    Authors: Albert P. Bartók, Sandip De, Carl Poelking, Noam Bernstein, James R. Kermode, Gábor Cśanyi, Michele Ceriotti - Science Advances 2017 cited by 772

  13. Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon

    Authors: Yury Lysogorskiy, Cas van der Oord, Anton Bochkarev, Sarath Menon, Matteo Rinaldi, Thomas Hammerschmidt, Matous Mrovec, Aidan P. Thompson, Gábor Cśanyi, Christoph Ortner, Ralf Drautz - npj Computational Materials 2021 cited by 248

  14. Machine learning based interatomic potential for amorphous carbon

    Authors: Volker L. Deringer, Gábor Cśanyi - Physical review. B./Physical review. B 2017 cited by 679

  15. Machine learning potentials for extended systems: a perspective

    Authors: Jörg Behler, Gábor Cśanyi - The European Physical Journal B 2021 cited by 219

  16. MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules

    Authors: Dávid Péter Kovács, J. Harry Moore, Nicholas J. Browning, Ilyes Batatia, Joshua T. Horton, Yixuan Pu, Venkat Kapil, William C. Witt, Ioan-Bogdan Magdău, Daniel J. Cole, Gábor Cśanyi - Journal of the American Chemical Society 2025 cited by 160

  17. Gaussian approximation potentials: A brief tutorial introduction

    Authors: Albert P. Bartók, Gábor Cśanyi - International Journal of Quantum Chemistry 2015 cited by 610

  18. The design space of E(3)-equivariant atom-centred interatomic potentials

    Authors: Ilyes Batatia, Simon L. Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi - Nature Machine Intelligence, Nat. Mac. Intell. 2025 cited by 154

  19. Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE

    Authors: Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Cśanyi - Journal of Chemical Theory and Computation 2021 cited by 118

  20. Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

    Authors: Andrea Grisafi, David M. Wilkins, Gábor Cśanyi, Michele Ceriotti - Physical Review Letters 2018 cited by 318

  21. Incompleteness of Atomic Structure Representations

    Authors: Sergey N. Pozdnyakov, Michael J. Willatt, Albert P. Bartók, Christoph Ortner, Gábor Cśanyi, Michele Ceriotti - Physical Review Letters 2020 cited by 194

  22. Platelet-derived HMGB1 is a critical mediator of thrombosis

    Authors: Sebastian Vogel, Rebecca Bodenstein, Qiwei Chen, Susanne Feil, Robert Feil, Johannes Rheinlaender, Tilman E. Schäffer, Erwin Bohn, Julia-Stefanie Frick, Oliver Borst, Patrick Münzer, Britta Walker, Justin Markel, Gábor Csányi, Patrick J. Pagano, Patricia Loughran, Morgan Jessup, Simon C. Watkins, Grant C. Bullock, Jason L. Sperry, Brian S. Zuckerbraun, Timothy R. Billiar, Michael T. Lotze, Meinrad Gawaz, Matthew D. Neal - Journal of Clinical Investigation 2015 cited by 368

  23. Identification of novel macropinocytosis inhibitors using a rational screen of Food and Drug Administration‐approved drugs

    Authors: Hui‐Ping Lin, Bhupesh Singla, Pushpankur Ghoshal, Jessica L Faulkner, Mary Cherian‐Shaw, Paul O’Connor, Jin‐Xiong She, Eric J. Belin de Chantemèle, Gábor Csányi - British Journal of Pharmacology 2018 cited by 139

  24. Origins of structural and electronic transitions in disordered silicon

    Authors: Volker L. Deringer, Noam Bernstein, Gábor Cśanyi, Chiheb Ben Mahmoud, Michele Ceriotti, Mark Wilson, D. A. Drabold, Stephen R. Elliott - Nature 2021 cited by 358