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11 results in Authors: Gabor Csanyi

Theoretical and Computational Chemistry

A Machine Learning Based Intramolecular Potential for a Flexible Organic Molecule

Daniel Cole, Letif Mones, Gábor Csányi

1,262

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Theoretical and Computational Chemistry

Machine Learning Force Field for Molecular Liquids: EC/EMC Binary Solvent

Ioan-Bogdan Magdău, Daniel J Arismendi-Arrieta, Holly E Smith, Clare P Grey, Kersti Hermansson, Gábor Csányi

1,636

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Materials Science

Workflows for Artificial Intelligence

Jörg Behler, Gábor Csányi, Lucas Foppa, Kisung Kang, Marcel F. Langer, Johannes T. Margraf, Akhil S. Nair, Thomas A. R. Purcell, Patrick Rinke, Matthias Scheffler, Alexandre Tkatchenko, Milica Todorović, Oliver T. Unke, Yi Yao

1,062

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Theoretical and Computational Chemistry

Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules

Elena Gelžinytė, Mario Öeren, Matthew D. Segall, Gábor Csányi

597

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Theoretical and Computational Chemistry

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

David Peter Kovacs, Cas van der Oord, Jiri Kucera, Alice Allen, Daniel Cole, Christoph Ortner, Gabor Csanyi

2,151

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Theoretical and Computational Chemistry

Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration

Sina Stocker, Hyunwook Jung, Gábor Csányi, C. Franklin Goldsmith, Karsten Reuter, Johannes T. Margraf

743

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Citations

Physical Chemistry

17O NMR spectroscopy reveals CO2 speciation and dynamics in hydroxide-based carbon capture materials.

Benjamin Rhodes, Lars Schaaf, Mary Zick, Suzi Pugh, Jordon Hilliard, Shivani Sharma, Casey Wade, Gabor Csanyi, Phillip Milner, Alex Forse

798

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Physical Chemistry

Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023

Igor Poltavsky, Anton Charkin-Gorbulin, Mirela Puleva, Gregory Cordeiro Fonseca, Ilyes Batatia, Nicholas J. Browning, Stefan Chmiela, Mengnan Cui, J. Thorben Frank, Stefan Heinen, Bing Huang, Silvan Käser, Adil Kabylda, Danish Khan, Carolin Müller, Alastair J. A. Price, Kai Riedmiller, Kai Töpfer, Tsz Wai Ko, Markus Meuwly, Matthias Rupp, Gabor Csanyi, O. Anatole von Lilienfeld, Johannes T. Margraf, Klaus-Robert Müller, Alexandre Tkatchenko

947

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Physical Chemistry

Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Molecular Dynamics in the TEA Challenge 2023

Igor Poltavsky, Mirela Puleva, Anton Charkin-Gorbulin, Gregory Cordeiro Fonseca, Ilyes Batatia, Nicholas J. Browning, Stefan Chmiela, Mengnan Cui, J. Thorben Frank, Stefan Heinen, Bing Huang, Silvan Käser, Adil Kabylda, Danish Khan, Carolin Müller, Alastair J. A. Price, Kai Riedmiller, Kai Töpfer, Tsz Wai Ko, Markus Meuwly, Matthias Rupp, Gabor Csanyi, O. Anatole von Lilienfeld, Johannes T. Margraf, Klaus-Robert Müller, Alexandre Tkatchenko

905

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Theoretical and Computational Chemistry

Kernel Charge Equilibration: Efficient and Accurate Prediction of Molecular Dipole Moments with a Machine-Learning Enhanced Electron Density Model

Carsten Staacke, Simon Wengert, Christian Kunkel, Gábor Csányi, Karsten Reuter, Johannes T. Margraf

675

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11 results in Gabor Csanyi in Authors: Gabor Csanyi

A Machine Learning Based Intramolecular Potential for a Flexible Organic Molecule

Machine Learning Force Field for Molecular Liquids: EC/EMC Binary Solvent

Workflows for Artificial Intelligence

Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration

17O NMR spectroscopy reveals CO2 speciation and dynamics in hydroxide-based carbon capture materials.

Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023

Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Molecular Dynamics in the TEA Challenge 2023

Kernel Charge Equilibration: Efficient and Accurate Prediction of Molecular Dipole Moments with a Machine-Learning Enhanced Electron Density Model

11 results in Authors: Gabor Csanyi