Suchergebnisse - Csányi, Gábor

Resonances in reflective Hamiltonian Monte Carlo von Kroupa, Namu, Csányi, Gábor, Handley, Will

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Guest Editorial: Special Topic on Software for Atomistic Machine Learning von Rupp, Matthias, Küçükbenli, Emine, Csányi, Gábor

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Bond-Network Entropy Governs Heat Transport in Coordination-Disordered Solids von Iwanowski, Kamil, Csányi, Gábor, Simoncelli, Michele

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Many-Body Coarse-Grained Molecular Dynamics with the Atomic Cluster Expansion von Wang, Yangshuai, Csanyi, Gabor, Ortner, Christoph

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Random Spin Committee Approach For Smooth Interatomic Potentials von Cărare, Vlad, Deringer, Volker L., Csányi, Gábor

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Better without U: Impact of Selective Hubbard U Correction on Foundational MLIPs von Warford, Thomas, Thiemann, Fabian L., Csányi, Gábor

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Computing solvation free energies of small molecules with experimental accuracy von Moore, J. Harry, Cole, Daniel J., Csanyi, Gabor

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Thermal Conductivity Predictions with Foundation Atomistic Models von Póta, Balázs, Ahlawat, Paramvir, Csányi, Gábor, Simoncelli, Michele

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How Accurate Are DFT Forces? Unexpectedly Large Uncertainties in Molecular Datasets von Kuryla, Domantas, Berger, Fabian, Csányi, Gábor, Michaelides, Angelos

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Self-consistent Coulomb interactions for machine learning interatomic potentials von Thomas, Jack, Baldwin, William J., Csányi, Gábor, Ortner, Christoph

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Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials von Grega, Ivan, Batatia, Ilyes, Csányi, Gábor, Karlapati, Sri, Deshpande, Vikram S.

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Symmetry Breaking in the Superionic Phase of Silver-Iodide von Hajibabaei, Amir, Baldwin, William J., Csányi, Gábor, Cox, Stephen J.

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Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials von Baldwin, William J., Batatia, Ilyes, Vondrák, Martin, Margraf, Johannes T., Csányi, Gábor

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Transferability of datasets between Machine-Learning Interaction Potentials von Niblett, Samuel P., Kourtis, Panagiotis, Magdău, Ioan-Bogdan, Grey, Clare P., Csányi, Gábor

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Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis von Karimitari, Nima, Clary, Jacob, Vigil-Fowler, Derek, Sundararaman, Ravishankar, Csányi, Gábor, Sutton, Christopher

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Equivariant Matrix Function Neural Networks von Batatia, Ilyes, Schaaf, Lars L., Chen, Huajie, Csányi, Gábor, Ortner, Christoph, Faber, Felix A.

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Zero Shot Molecular Generation via Similarity Kernels von Elijošius, Rokas, Zills, Fabian, Batatia, Ilyes, Norwood, Sam Walton, Kovács, Dávid Péter, Holm, Christian, Csányi, Gábor

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Regularity Priors for the Linear Atomic Cluster Expansion von Darby, James P., Morrow, Joe D., Bartók, Albert P., Deringer, Volker L., Csányi, Gábor, Ortner, Christoph

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Efficient Composite Infrared Spectroscopy: Combining the Doubly-Harmonic Approximation with Machine Learning Potentials von Pracht, Philipp, Pillai, Yuthika, Kapil, Venkat, Csányi, Gábor, Gönnheimer, Nils, Vondrák, Martin, Margraf, Johannes T., Wales, David J.

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Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces von Stark, Wojciech G., van der Oord, Cas, Batatia, Ilyes, Zhang, Yaolong, Jiang, Bin, Csányi, Gábor, Maurer, Reinhard J.

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