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Theoretical and Computational Chemistry

Working Paper

Size-Extensive Molecular Machine Learning with Global Descriptors

Hyunwook Jung, Sina Stocker, Christian Kunkel, Harald Oberhofer, Byungchan Han, Karsten Reuter, Johannes T. Margraf

Version 1 posted 22 October 2019

820

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1

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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

636

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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

442

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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

643

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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

376

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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

1,171

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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

1,094

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Theoretical and Computational Chemistry

Working Paper

Machine-Learning Driven Global Optimization of Surface Adsorbate Geometries

Hyunwook Jung, Lena Sauerland, Sina Stocker, Karsten Reuter, Johannes T. Margraf

Version 3 posted 08 June 2023

734

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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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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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Refine Search

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

Size-Extensive Molecular Machine Learning with Global Descriptors

Machine-Learning Coupled Cluster Properties through a Density Tensor Representation

Physics-Inspired Machine Learning of Localized Intensive Properties

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

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

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

Machine-Learning Driven Global Optimization of Surface Adsorbate Geometries

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

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