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Theoretical and Computational Chemistry

Working Paper

Machine-Learning Coupled Cluster Properties through a Density Tensor Representation

Benjamin Peyton, Connor Briggs, Ruhee D'Cunha, Johannes T. Margraf, Thomas Crawford

Version 1 posted 02 April 2020

641

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Theoretical and Computational Chemistry

Working Paper

Physics-Inspired Machine Learning of Localized Intensive Properties

Ke Chen, Christian Kunkel, Bingqing Cheng, Karsten Reuter, Johannes T. Margraf

Version 1 posted 21 February 2023

445

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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

275

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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

558

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1

Citations

Theoretical and Computational Chemistry

Working Paper

q-pac: A Python Package for Machine Learned Charge Equilibration Models

Martin Vondrák, Karsten Reuter, Johannes T. Margraf

Version 1 posted 04 May 2023

383

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Materials Science

Working Paper

Interplay between shape and composition in bimetallic nanoparticles revealed by an efficient optimal-exchange optimization algorithm

Felix Neumann, Johannes T Margraf, Karsten Reuter, Albert Bruix

Version 1 posted 23 December 2021

179

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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

391

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Theoretical and Computational Chemistry

Working Paper

On the role of long-range electrostatics in machine-learned interatomic potentials for complex battery materials

Carsten G. Staacke, Hendrik H. Heenen, Christoph Scheurer, Gábor Csányi, Karsten Reuter, Johannes T. Margraf

Version 1 posted 26 July 2021

646

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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

241

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Theoretical and Computational Chemistry

Working Paper

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

Version 1 posted 25 November 2021

597

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19 results in Johannes T Margraf in Authors: Johannes T Margraf

Machine-Learning Coupled Cluster Properties through a Density Tensor Representation

Physics-Inspired Machine Learning of Localized Intensive Properties

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

Heterogeneous Catalysts in Grammar School

q-pac: A Python Package for Machine Learned Charge Equilibration Models

Interplay between shape and composition in bimetallic nanoparticles revealed by an efficient optimal-exchange optimization algorithm

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

On the role of long-range electrostatics in machine-learned interatomic potentials for complex battery materials

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

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

19 results in Authors: Johannes T Margraf