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Johannes T Margraf

Showing results (1-10 of 67) with videos related to

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Journal of Computational Chemistry|April 24, 2024
Neural graph distance embedding for molecular geometry generationJohannes T Margraf
Angewandte Chemie (International Ed. in English)|March 10, 2023
Science-Driven Atomistic Machine LearningJohannes T Margraf
The Journal of Physical Chemistry. A|July 10, 2018
Making the Coupled Cluster Correlation Energy Machine-LearnableJohannes T Margraf, Karsten Reuter
The Journal of Chemical Physics|June 17, 2018
Communication: Coupled cluster and many-body perturbation theory for fractional charges and spinsJohannes T Margraf, Rodney Bartlett
Nature Communications|January 13, 2021
Pure non-local machine-learned density functional theory for electron correlationJohannes T Margraf, Karsten Reuter
ACS Omega|August 29, 2019
Systematic Enumeration of Elementary Reaction Steps in Surface CatalysisJohannes T Margraf, Karsten Reuter
Journal of Molecular Modeling|April 18, 2019
What is semiempirical molecular orbital theory approximating?Johannes T Margraf, Pavlo O Dral
The Journal of Chemical Physics|August 2, 2023
q-pac: A Python package for machine learned charge equilibration modelsMartin Vondrák, Karsten Reuter, Johannes T Margraf
The Journal of Chemical Physics|July 1, 2019
Towards density functional approximations from coupled cluster correlation energy densitiesJohannes T Margraf, Christian Kunkel, Karsten Reuter
Journal of Chemical Theory and Computation|April 25, 2025
Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic DifferentiationNils Gönnheimer, Karsten Reuter, Johannes T Margraf
Pageof 7

Showing results (1-10 of 67) with videos related to

Sort By:
Pageof 7
Journal of Computational Chemistry|April 24, 2024
Neural graph distance embedding for molecular geometry generationJohannes T Margraf
Angewandte Chemie (International Ed. in English)|March 10, 2023
Science-Driven Atomistic Machine LearningJohannes T Margraf
The Journal of Physical Chemistry. A|July 10, 2018
Making the Coupled Cluster Correlation Energy Machine-LearnableJohannes T Margraf, Karsten Reuter
The Journal of Chemical Physics|June 17, 2018
Communication: Coupled cluster and many-body perturbation theory for fractional charges and spinsJohannes T Margraf, Rodney Bartlett
Nature Communications|January 13, 2021
Pure non-local machine-learned density functional theory for electron correlationJohannes T Margraf, Karsten Reuter
ACS Omega|August 29, 2019
Systematic Enumeration of Elementary Reaction Steps in Surface CatalysisJohannes T Margraf, Karsten Reuter
Journal of Molecular Modeling|April 18, 2019
What is semiempirical molecular orbital theory approximating?Johannes T Margraf, Pavlo O Dral
The Journal of Chemical Physics|August 2, 2023
q-pac: A Python package for machine learned charge equilibration modelsMartin Vondrák, Karsten Reuter, Johannes T Margraf
The Journal of Chemical Physics|July 1, 2019
Towards density functional approximations from coupled cluster correlation energy densitiesJohannes T Margraf, Christian Kunkel, Karsten Reuter
Journal of Chemical Theory and Computation|April 25, 2025
Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic DifferentiationNils Gönnheimer, Karsten Reuter, Johannes T Margraf
Pageof 7