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Gábor Csányi
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2020 – today
- 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
Zero Shot Molecular Generation via Similarity Kernels. CoRR abs/2402.08708 (2024) - [i10]Nima Karimitari
Accurate Crystal Structure Prediction of New 2D Hybrid Organic Inorganic Perovskites. CoRR abs/2403.06955 (2024) - 2023
- [j10]Martina Crippa
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
Equivariant Matrix Function Neural Networks. CoRR abs/2310.10434 (2023) - 2022
- [j9]Geneviève Dusson, Markus Bachmayr
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
Atomic permutationally invariant polynomials for fitting molecular force fields. Mach. Learn. Sci. Technol. 2(2): 25017 (2021) - [j6]Mario G. Zauchner
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
Regularised atomic body-ordered permutation-invariant polynomials for the construction of interatomic potentials. Mach. Learn. Sci. Technol. 1(1): 15004 (2020) - [i4]Ganesh Sivaraman
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
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
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
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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last updated on 2024-10-07 21:19 CEST by the dblp team
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