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Theoretical and Computational Chemistry

Working Paper

Towards Density Functional Approximations from Coupled Cluster Correlation Energy Densities

Johannes T. Margraf, Christian Kunkel, Karsten Reuter

Version 2 posted 30 April 2019

1,186

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Theoretical and Computational Chemistry

Working Paper

Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression

Chiara Panosetti, Artur Engelmann, Lydia Nemec, Karsten Reuter, Johannes T. Margraf

Version 1 posted 02 October 2019

1,119

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Catalysis

Working Paper

Discovering high entropy alloy electrocatalysts in vast composition spaces with multi-objective optimization

Wenbin Xu, Elias Diesen, Tianwei He, Karsten Reuter, Johannes T. Margraf

Version 1 posted 17 November 2023

443

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1

Citations

Physical Chemistry

Working Paper

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

Version 1 posted 27 September 2024

358

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Theoretical and Computational Chemistry

Working Paper

Small Basis Set Density Functional Theory Method for Cost-efficient, Large-scale Condensed Matter Simulations

Elisabeth Keller, Jack Morgenstein, Karsten Reuter, Johannes T. Margraf

Version 1 posted 20 June 2024

187

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Physical Chemistry

Working Paper

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

Version 1 posted 27 September 2024

467

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Materials Science

Review

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

Version 1 posted 13 November 2024

457

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Theoretical and Computational Chemistry

Working Paper

Obtaining Robust Density Functional Tight Binding Parameters for Solids Across the Periodic Table

Mengnan Cui, Karsten Reuter, Johannes T. Margraf

Version 1 posted 23 February 2024

425

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Theoretical and Computational Chemistry

Working Paper

Heterogeneous Catalysts in Grammar School

Johannes T. Margraf, Zachary W. Ulissi, Yousung Jung, Karsten Reuter

Version 1 posted 13 December 2021

578

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1

Citations

Materials Science

Working Paper

Tackling structural complexity in Li2S-P2S5 Solid-State Electrolytes using Machine Learning Potentials

Carsten G Staacke, Tabea Huss, Johannes T Margraf, Karsten Reuter, Christoph Scheurer

Version 1 posted 27 June 2022

251

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Refine Search

24 results in Johannes T. Margraf in Authors: Johannes T. Margraf

Towards Density Functional Approximations from Coupled Cluster Correlation Energy Densities

Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression

Discovering high entropy alloy electrocatalysts in vast composition spaces with multi-objective optimization

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

Small Basis Set Density Functional Theory Method for Cost-efficient, Large-scale Condensed Matter Simulations

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

Workflows for Artificial Intelligence

Obtaining Robust Density Functional Tight Binding Parameters for Solids Across the Periodic Table

Heterogeneous Catalysts in Grammar School

Tackling structural complexity in Li2S-P2S5 Solid-State Electrolytes using Machine Learning Potentials

24 results in Authors: Johannes T. Margraf