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The Journal of Physical Chemistry. A|July 10, 2018
Making the Coupled Cluster Correlation Energy Machine-LearnableJohannes T Margraf, Karsten ReuterNature Communications|January 13, 2021
Pure non-local machine-learned density functional theory for electron correlationJohannes T Margraf, Karsten ReuterACS Omega|August 29, 2019
Systematic Enumeration of Elementary Reaction Steps in Surface CatalysisJohannes T Margraf, Karsten ReuterThe Journal of Chemical Physics|July 1, 2019
Towards density functional approximations from coupled cluster correlation energy densitiesJohannes T Margraf, Christian Kunkel, Karsten ReuterJournal 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 MargrafJournal of Chemical Theory and Computation|June 12, 2024
Obtaining Robust Density Functional Tight-Binding Parameters for Solids across the Periodic TableMengnan Cui, Karsten Reuter, Johannes T MargrafThe Journal of Chemical Physics|August 2, 2023
q-pac: A Python package for machine learned charge equilibration modelsMartin Vondrák, Karsten Reuter, Johannes T MargrafThe Journal of Chemical Physics|January 16, 2022
Regularized second-order correlation methods for extended systemsElisabeth Keller, Theodoros Tsatsoulis, Karsten Reuter, et al.Chemical Science|June 24, 2021
Data-efficient machine learning for molecular crystal structure predictionSimon Wengert, Gábor Csányi, Karsten Reuter, et al.Nature Communications|October 31, 2020
Machine learning in chemical reaction spaceSina Stocker, Gábor Csányi, Karsten Reuter, et al.Pageof 21