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ORCIDGábor Csányi
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2020 – today
- 2025
[j11]Ilyes Batatia, Simon L. Batzner
, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi:
The design space of E(3)-equivariant atom-centred interatomic potentials. Nat. Mac. Intell. 7(1): 56-67 (2025)- [c5]Jesun Sahariar Firoz, Franco Pellegrini, Mario Geiger, Darren Hsu, Jenna A. Bilbrey, Han-Yi Chou, Maximilian Stadler, Markus Höhnerbach, Tingyu Wang, Dejun Lin, Emine Küçükbenli, Henry W. Sprueill, Ilyes Batatia, Sotiris S. Xantheas, MalSoon Lee, Christopher J. Mundy, Gábor Csányi, Justin S. Smith, Ponnuswamy Sadayappan, Sutanay Choudhury:
Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE. HPDC 2025: 8:1-8:13 - [i15]Ryan Jacobs, Dane Morgan, Siamak Attarian, Jun Meng, Chen Shen, Zhenghao Wu, Clare Yijia Xie, Julia H. Yang, Nongnuch Artrith, Ben Blaiszik, Gerbrand Ceder, Kamal Choudhary, Gábor Csányi, Ekin Dogus Cubuk, Bowen Deng, Ralf Drautz, Xiang Fu, Jonathan Godwin, Vasant G. Honavar, Olexandr Isayev, Anders Johansson, Boris Kozinsky, Stefano Martiniani, Shyue Ping Ong, Igor Poltavsky, K. J. Schmidt, So Takamoto, Aidan Thompson, Julia Westermayr, Brandon M. Wood:
A practical guide to machine learning interatomic potentials - Status and future. CoRR abs/2503.09814 (2025) - [i14]Jesun Firoz, Franco Pellegrini, Mario Geiger, Darren Hsu, Jenna A. Bilbrey, Han-Yi Chou, Maximilian Stadler, Markus Hoehnerbach, Tingyu Wang, Dejun Lin, Emine Küçükbenli, Henry W. Sprueill, Ilyes Batatia, Sotiris S. Xantheas, MalSoon Lee, Chris Mundy, Gábor Csányi, Justin S. Smith, Ponnuswamy Sadayappan, Sutanay Choudhury:
Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE. CoRR abs/2504.10700 (2025) - [i13]Namu Kroupa, Gábor Csányi, Will Handley:
Resonances in reflective Hamiltonian Monte Carlo. CoRR abs/2504.12374 (2025) - 2024
- [c4]Ilyes Batatia, Lars L. Schaaf, Gábor Csányi, Christoph Ortner, Felix A. Faber:
Equivariant Matrix Function Neural Networks. ICLR 2024 - [c3]Ivan Grega, Ilyes Batatia, Gábor Csányi, Sri Karlapati, Vikram S. Deshpande:
Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials. ICLR 2024 - [i12]Ivan Grega, Ilyes Batatia, Gábor Csányi, Sri Karlapati, Vikram S. Deshpande:
Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials. CoRR abs/2401.16914 (2024) - [i11]Rokas Elijosius, Fabian Zills, Ilyes Batatia, Sam Walton Norwood, Dávid Péter Kovács, Christian Holm, Gábor Csányi:
Zero Shot Molecular Generation via Similarity Kernels. CoRR abs/2402.08708 (2024) - [i10]Nima Karimitari, William J. Baldwin, Evan W. Muller, Zachary J. L. Bare, W. Joshua Kennedy, Gábor Csányi, Christopher Sutton:
Accurate Crystal Structure Prediction of New 2D Hybrid Organic Inorganic Perovskites. CoRR abs/2403.06955 (2024) - 2023
- [j10]Martina Crippa, Annalisa Cardellini, Matteo Cioni, Gábor Csányi, Giovanni M. Pavan:
Machine learning of microscopic structure-dynamics relationships in complex molecular systems. Mach. Learn. Sci. Technol. 4(4): 45044 (2023) - [i9]Ilyes Batatia, Lars L. Schaaf, Huajie Chen, Gábor Csányi, Christoph Ortner, Felix A. Faber:
Equivariant Matrix Function Neural Networks. CoRR abs/2310.10434 (2023) - 2022
- [j9]Geneviève Dusson, Markus Bachmayr, Gábor Csányi, Ralf Drautz, Simon Etter, Cas van der Oord, Christoph Ortner:
Atomic cluster expansion: Completeness, efficiency and stability. J. Comput. Phys. 454: 110946 (2022) - [j8]Carsten G. Staacke, Simon Wengert, Christian Kunkel, Gábor Csányi, Karsten Reuter, Johannes T. Margraf:
Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model. Mach. Learn. Sci. Technol. 3(1): 15032 (2022) - [c2]Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, Gábor Csányi:
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields. NeurIPS 2022 - [i8]Ilyes Batatia, Simon L. Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi:
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials. CoRR abs/2205.06643 (2022) - [i7]Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, Gábor Csányi:
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields. CoRR abs/2206.07697 (2022) - [i6]James P. Darby, Dávid Péter Kovács, Ilyes Batatia, Miguel A. Caro, Gus L. W. Hart, Christoph Ortner, Gábor Csányi:
Tensor-reduced atomic density representations. CoRR abs/2210.01705 (2022) - 2021
- [j7]Alice E. A. Allen, Geneviève Dusson, Christoph Ortner, Gábor Csányi:
Atomic permutationally invariant polynomials for fitting molecular force fields. Mach. Learn. Sci. Technol. 2(2): 25017 (2021) - [j6]Mario G. Zauchner, Stefano Dal Forno, Gábor Csányi, Andrew P. Horsfield, Johannes Lischner:
Predicting polarizabilities of silicon clusters using local chemical environments. Mach. Learn. Sci. Technol. 2(4): 45029 (2021) - [c1]Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, José Miguel Hernández-Lobato:
Symmetry-Aware Actor-Critic for 3D Molecular Design. ICLR 2021 - [i5]Aldo Glielmo, Claudio Zeni, Bingqing Cheng, Gábor Csányi, Alessandro Laio:
Ranking the information content of distance measures. CoRR abs/2104.15079 (2021) - 2020
- [j5]Cas van der Oord, Geneviève Dusson, Gábor Csányi, Christoph Ortner:
Regularised atomic body-ordered permutation-invariant polynomials for the construction of interatomic potentials. Mach. Learn. Sci. Technol. 1(1): 15004 (2020) - [i4]Ganesh Sivaraman, Leighanne Gallington, Anand Narayanan Krishnamoorthy, Marius Stan, Gábor Csányi, Álvaro Vázquez-Mayagoitia, Chris J. Benmore:
An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide. CoRR abs/2009.04045 (2020) - [i3]Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, José Miguel Hernández-Lobato:
Symmetry-Aware Actor-Critic for 3D Molecular Design. CoRR abs/2011.12747 (2020)
2010 – 2019
- 2019
- [i2]Ganesh Sivaraman, Anand Narayanan Krishnamoorthy, Matthias Baur, Christian Holm, Marius Stan, Gábor Csányi, Chris J. Benmore, Álvaro Vázquez-Mayagoitia:
Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide. CoRR abs/1910.10254 (2019) - [i1]Markus Bachmayr, Gábor Csányi, Geneviève Dusson, Simon Etter, Cas van der Oord, Christoph Ortner:
Approximation of Potential Energy Surfaces with Spherical Harmonics. CoRR abs/1911.03550 (2019) - 2016
- [j4]Nikolas S. Burkoff, Robert J. N. Baldock, Csilla Várnai, David L. Wild, Gábor Csányi:
Exploiting molecular dynamics in Nested Sampling simulations of small peptides. Comput. Phys. Commun. 201: 8-18 (2016) - 2015
- [j3]Letif Mones, Andrew Jones, Andreas W. Götz, Teodoro Laino, Ross C. Walker, Ben Leimkuhler, Gábor Csányi, Noam Bernstein:
The adaptive buffered force QM/MM method in the CP2K and AMBER software packages. J. Comput. Chem. 36(9): 633-648 (2015) - 2011
- [j2]Brendon J. Brewer, Lívia B. Pártay, Gábor Csányi:
Diffusive nested sampling. Stat. Comput. 21(4): 649-656 (2011)
2000 – 2009
- 2007
- [j1]Olga Obrezanova, Gábor Csányi, Joelle M. R. Gola, Matthew D. Segall:
Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties. J. Chem. Inf. Model. 47(5): 1847-1857 (2007)
Coauthor Index
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