ERef Bayreuth
Institutionen der Universität Bayreuth
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- Universität Bayreuth (59213)
- Fakultäten (55596)
- Fakultät für Biologie, Chemie und Geowissenschaften (16801)
- Fachgruppe Chemie (4996)
- Lehrstuhl Physikalische Chemie V - Theorie und Maschinelles Lernen (37)
- Fachgruppe Chemie (4996)
- Fakultät für Biologie, Chemie und Geowissenschaften (16801)
- Fakultäten (55596)
Anzahl der Einträge auf dieser Ebene: 37.
B
Bruix, Albert ; Margraf, Johannes T. ; Andersen, Mie ; Reuter, Karsten:
First-principles-based multiscale modelling of heterogeneous catalysis.
In: Nature Catalysis. Bd. 2 (2019) . - S. 659-670.
DOI: https://doi.org/10.1038/s41929-019-0298-3
First-principles-based multiscale modelling of heterogeneous catalysis.
In: Nature Catalysis. Bd. 2 (2019) . - S. 659-670.
DOI: https://doi.org/10.1038/s41929-019-0298-3
C
Chen, Ke ; Kunkel, Christian ; Cheng, Bingqing ; Reuter, Karsten ; Margraf, Johannes T.:
Physics-Inspired Machine Learning of Localized Intensive Properties.
In: Chemical Science. Bd. 14 (2023) Heft 18 . - S. 4913-4922.
DOI: https://doi.org/10.1039/D3SC00841J
Physics-Inspired Machine Learning of Localized Intensive Properties.
In: Chemical Science. Bd. 14 (2023) Heft 18 . - S. 4913-4922.
DOI: https://doi.org/10.1039/D3SC00841J
Chen, Ke ; Kunkel, Christian ; Reuter, Karsten ; Margraf, Johannes T.:
Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening.
In: Digital Discovery. Bd. 1 (2022) Heft 2 . - S. 147-157.
DOI: https://doi.org/10.1039/D1DD00038A
Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening.
In: Digital Discovery. Bd. 1 (2022) Heft 2 . - S. 147-157.
DOI: https://doi.org/10.1039/D1DD00038A
Cheng, Bingqing ; Griffiths, Ryan-Rhys ; Wengert, Simon ; Kunkel, Christian ; Stenczel, Tamas ; Zhu, Bonan ; Deringer, Volker L. ; Bernstein, Noam ; Margraf, Johannes T. ; Reuter, Karsten ; Csányi, Gábor:
Mapping Materials and Molecules.
In: Accounts of Chemical Research. Bd. 53 (2020) Heft 9 . - S. 1981-1991.
DOI: https://doi.org/10.1021/acs.accounts.0c00403
Mapping Materials and Molecules.
In: Accounts of Chemical Research. Bd. 53 (2020) Heft 9 . - S. 1981-1991.
DOI: https://doi.org/10.1021/acs.accounts.0c00403
Cadranel, Alejandro ; Margraf, Johannes T. ; Strauss, Volker ; Clark, Timothy ; Guldi, Dirk M.:
Carbon Nanodots for Charge-Transfer Processes.
In: Accounts of Chemical Research. Bd. 52 (2019) Heft 4 . - S. 955-963.
DOI: https://doi.org/10.1021/acs.accounts.8b00673
Carbon Nanodots for Charge-Transfer Processes.
In: Accounts of Chemical Research. Bd. 52 (2019) Heft 4 . - S. 955-963.
DOI: https://doi.org/10.1021/acs.accounts.8b00673
J
Jung, Hyunwook ; Sauerland, Lena ; Stocker, Sina ; Reuter, Karsten ; Margraf, Johannes T.:
Machine-Learning Driven Global Optimization of Surface Adsorbate Geometries.
In: npj Computational Materials. Bd. 9 (2023) . - 114.
DOI: https://doi.org/10.1038/s41524-023-01065-w
Machine-Learning Driven Global Optimization of Surface Adsorbate Geometries.
In: npj Computational Materials. Bd. 9 (2023) . - 114.
DOI: https://doi.org/10.1038/s41524-023-01065-w
Jung, Hyunwook ; Stocker, Sina ; Kunkel, Christian ; Oberhofer, Harald ; Han, Byungchan ; Reuter, Karsten ; Margraf, Johannes T.:
Size-extensive molecular machine learning with global representations.
In: ChemSystemsChem. Bd. 2 (2020) Heft 4 . - e1900052.
DOI: https://doi.org/10.1002/syst.201900052
Size-extensive molecular machine learning with global representations.
In: ChemSystemsChem. Bd. 2 (2020) Heft 4 . - e1900052.
DOI: https://doi.org/10.1002/syst.201900052
K
Kube, Pierre ; Dong, Jinhu ; Bastardo, Nuria Sánchez ; Ruland, Holger ; Schlögl, Robert ; Margraf, Johannes T. ; Reuter, Karsten ; Trunschke, Annette:
Green synthesis of propylene oxide directly from propane.
In: Nature Communications. Bd. 13 (2022) . - 7504.
DOI: https://doi.org/10.1038/s41467-022-34967-2
Green synthesis of propylene oxide directly from propane.
In: Nature Communications. Bd. 13 (2022) . - 7504.
DOI: https://doi.org/10.1038/s41467-022-34967-2
Keller, Elisabeth ; Tsatsoulis, Theodoros ; Reuter, Karsten ; Margraf, Johannes T.:
Regularized Second-Order Correlation Methods for Extended Systems.
In: The Journal of Chemical Physics. Bd. 156 (2022) . - 024106.
DOI: https://doi.org/10.1063/5.0078119
Regularized Second-Order Correlation Methods for Extended Systems.
In: The Journal of Chemical Physics. Bd. 156 (2022) . - 024106.
DOI: https://doi.org/10.1063/5.0078119
Kunkel, Christian ; Margraf, Johannes T. ; Chen, Ke ; Oberhofer, Harald ; Reuter, Karsten:
Active discovery of organic semiconductors.
In: Nature Communications. Bd. 12 (2021) . - 2422.
DOI: https://doi.org/10.1038/s41467-021-22611-4
Active discovery of organic semiconductors.
In: Nature Communications. Bd. 12 (2021) . - 2422.
DOI: https://doi.org/10.1038/s41467-021-22611-4
Klicpera, J. ; Giri, S. ; Margraf, Johannes T. ; Günnemann, S.:
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules.
2020
Veranstaltung: Workshop on Machine Learning for Molecules, NeurIPS 2020 .
(Veranstaltungsbeitrag: Workshop , Paper )
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules.
2020
Veranstaltung: Workshop on Machine Learning for Molecules, NeurIPS 2020 .
(Veranstaltungsbeitrag: Workshop , Paper )
Kunkel, Christian ; Schober, Christoph ; Margraf, Johannes T. ; Reuter, Karsten ; Oberhofer, Harald:
Finding the right bricks for molecular legos : A data mining approach to organic semiconductor design.
In: Chemistry of Materials. Bd. 31 (2019) Heft 3 . - S. 969-978.
DOI: https://doi.org/10.1021/acs.chemmater.8b04436
Finding the right bricks for molecular legos : A data mining approach to organic semiconductor design.
In: Chemistry of Materials. Bd. 31 (2019) Heft 3 . - S. 969-978.
DOI: https://doi.org/10.1021/acs.chemmater.8b04436
L
Li, Haobo ; Liu, Yunxia ; Chen, Ke ; Margraf, Johannes T. ; Li, Youyong ; Reuter, Karsten:
Subgroup discovery points to the prominent role of charge transfer in breaking nitrogen scaling relations at single-atom catalysts on VS₂.
In: ACS Catalysis. Bd. 11 (2021) Heft 13 . - S. 7906-7914.
DOI: https://doi.org/10.1021/acscatal.1c01324
Subgroup discovery points to the prominent role of charge transfer in breaking nitrogen scaling relations at single-atom catalysts on VS₂.
In: ACS Catalysis. Bd. 11 (2021) Heft 13 . - S. 7906-7914.
DOI: https://doi.org/10.1021/acscatal.1c01324
M
Margraf, Johannes T.:
Neural graph distance embedding for molecular geometry generation.
In: Journal of Computational Chemistry. Bd. 45 (2024) Heft 21 . - S. 1784-1790.
DOI: https://doi.org/10.15495/EPub_UBT_00007960
Neural graph distance embedding for molecular geometry generation.
In: Journal of Computational Chemistry. Bd. 45 (2024) Heft 21 . - S. 1784-1790.
DOI: https://doi.org/10.15495/EPub_UBT_00007960
Margraf, Johannes T. ; Jung, Hyunwook ; Scheurer, Christoph ; Reuter, Karsten:
Exploring catalytic reaction networks with machine learning.
In: Nature Catalysis. Bd. 6 (2023) . - S. 112-121.
DOI: https://doi.org/10.1038/s41929-022-00896-y
Exploring catalytic reaction networks with machine learning.
In: Nature Catalysis. Bd. 6 (2023) . - S. 112-121.
DOI: https://doi.org/10.1038/s41929-022-00896-y
Margraf, Johannes T.:
Science-Driven Atomistic Machine Learning.
In: Angewandte Chemie International Edition. Bd. 62 (2023) Heft 26 . - e202219170.
DOI: https://doi.org/10.1002/anie.202219170
Science-Driven Atomistic Machine Learning.
In: Angewandte Chemie International Edition. Bd. 62 (2023) Heft 26 . - e202219170.
DOI: https://doi.org/10.1002/anie.202219170
Margraf, Johannes T. ; Ulissi, Zachary W. ; Jung, Yousung ; Reuter, Karsten:
Heterogeneous Catalysis in Grammar School.
In: The Journal of Physical Chemistry C. Bd. 126 (2022) Heft 6 . - S. 2931-2936.
DOI: https://doi.org/10.1021/acs.jpcc.1c10285
Heterogeneous Catalysis in Grammar School.
In: The Journal of Physical Chemistry C. Bd. 126 (2022) Heft 6 . - S. 2931-2936.
DOI: https://doi.org/10.1021/acs.jpcc.1c10285
Margraf, Johannes T. ; Reuter, Karsten:
Pure non-local machine-learned density functional theory for electron correlation.
In: Nature Communications. Bd. 12 (2021) . - 344.
DOI: https://doi.org/10.1038/s41467-020-20471-y
Pure non-local machine-learned density functional theory for electron correlation.
In: Nature Communications. Bd. 12 (2021) . - 344.
DOI: https://doi.org/10.1038/s41467-020-20471-y
Margraf, Johannes T. ; Hennemann, Matthias ; Clark, Timothy:
EMPIRE: A highly parallel semiempirical molecular orbital program: 3: Born-Oppenheimer molecular dynamics.
In: Journal of Molecular Modeling. Bd. 26 (2020) . - 43.
DOI: https://doi.org/10.1007/s00894-020-4293-z
EMPIRE: A highly parallel semiempirical molecular orbital program: 3: Born-Oppenheimer molecular dynamics.
In: Journal of Molecular Modeling. Bd. 26 (2020) . - 43.
DOI: https://doi.org/10.1007/s00894-020-4293-z
Margraf, Johannes T. ; Reuter, Karsten:
Systematic Enumeration of Elementary Reaction Steps in Surface Catalysis.
In: ACS Omega. Bd. 4 (2019) Heft 2 . - S. 3370-3379.
DOI: https://doi.org/10.1021/acsomega.8b03200
Systematic Enumeration of Elementary Reaction Steps in Surface Catalysis.
In: ACS Omega. Bd. 4 (2019) Heft 2 . - S. 3370-3379.
DOI: https://doi.org/10.1021/acsomega.8b03200
Margraf, Johannes T. ; Kunkel, Christian ; Reuter, Karsten:
Towards Density Functional Approximations from Coupled Cluster Correlation Energy Densities.
In: The Journal of Chemical Physics. Bd. 150 (2019) . - 244116.
DOI: https://doi.org/10.1063/1.5094788
Towards Density Functional Approximations from Coupled Cluster Correlation Energy Densities.
In: The Journal of Chemical Physics. Bd. 150 (2019) . - 244116.
DOI: https://doi.org/10.1063/1.5094788
Margraf, Johannes T. ; Dral, Pavlo O.:
What is semiempirical molecular orbital theory approximating?
In: Journal of Molecular Modeling. Bd. 25 (2019) . - 119.
DOI: https://doi.org/10.1007/s00894-019-4005-8
What is semiempirical molecular orbital theory approximating?
In: Journal of Molecular Modeling. Bd. 25 (2019) . - 119.
DOI: https://doi.org/10.1007/s00894-019-4005-8
P
Panosetti, Chiara ; Engelmann, Artur ; Nemec, Lydia ; Reuter, Karsten ; Margraf, Johannes T.:
Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression.
In: Journal of Chemical Theory and Computation. Bd. 16 (2020) Heft 4 . - S. 2181-2191.
DOI: https://doi.org/10.1021/acs.jctc.9b00975
Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression.
In: Journal of Chemical Theory and Computation. Bd. 16 (2020) Heft 4 . - S. 2181-2191.
DOI: https://doi.org/10.1021/acs.jctc.9b00975
Peyton, Benjamin G. ; Briggs, Connor ; D'Cunha, Ruhee ; Margraf, Johannes T. ; Crawford, T. Daniel:
Machine-Learning Coupled Cluster Properties through a Density Tensor Representation.
In: The Journal of Physical Chemistry A. Bd. 124 (2020) Heft 23 . - S. 4861-4871.
DOI: https://doi.org/10.1021/acs.jpca.0c02804
Machine-Learning Coupled Cluster Properties through a Density Tensor Representation.
In: The Journal of Physical Chemistry A. Bd. 124 (2020) Heft 23 . - S. 4861-4871.
DOI: https://doi.org/10.1021/acs.jpca.0c02804
R
Ranasinghe, Duminda S. ; Margraf, Johannes T. ; Perera, Ajith ; Bartlett, Rodney J.:
Vertical Valence Ionization Potential Benchmarks from Equation-of-Motion Coupled Cluster Theory and QTP Functionals.
In: The Journal of Chemical Physics. Bd. 150 (2019) Heft 7 . - 074108.
DOI: https://doi.org/10.1063/1.5084728
Vertical Valence Ionization Potential Benchmarks from Equation-of-Motion Coupled Cluster Theory and QTP Functionals.
In: The Journal of Chemical Physics. Bd. 150 (2019) Heft 7 . - 074108.
DOI: https://doi.org/10.1063/1.5084728
S
Stocker, Sina ; Jung, Hyunwook ; Csányi, Gábor ; Goldsmith, C. Franklin ; Reuter, Karsten ; Margraf, Johannes T.:
Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration.
In: Journal of Chemical Theory and Computation. Bd. 19 (2023) Heft 19 . - S. 6796-6804.
DOI: https://doi.org/10.1021/acs.jctc.3c00541
Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration.
In: Journal of Chemical Theory and Computation. Bd. 19 (2023) Heft 19 . - S. 6796-6804.
DOI: https://doi.org/10.1021/acs.jctc.3c00541
Stocker, Sina ; Gasteiger, Johannes ; Becker, Florian ; Günnemann, Stephan ; Margraf, Johannes T.:
How Robust are Modern Graph Neural Network Potentials in Long and Hot Molecular Dynamics Simulations?
In: Machine Learning: Science and Technology. Bd. 3 (2022) Heft 4 . - 045010.
DOI: https://doi.org/10.1088/2632-2153/ac9955
How Robust are Modern Graph Neural Network Potentials in Long and Hot Molecular Dynamics Simulations?
In: Machine Learning: Science and Technology. Bd. 3 (2022) Heft 4 . - 045010.
DOI: https://doi.org/10.1088/2632-2153/ac9955
Staacke, Carsten G. ; Wengert, Simon ; Kunkel, Christian ; Csányi, Gábor ; Reuter, Karsten ; Margraf, Johannes T.:
Kernel Charge Equilibration: Efficient and Accurate Prediction of Molecular Dipole Moments with a Machine-Learning Enhanced Electron Density Model.
In: Machine Learning: Science and Technology. Bd. 3 (2022) Heft 1 . - 015032.
DOI: https://doi.org/10.1088/2632-2153/ac568d
Kernel Charge Equilibration: Efficient and Accurate Prediction of Molecular Dipole Moments with a Machine-Learning Enhanced Electron Density Model.
In: Machine Learning: Science and Technology. Bd. 3 (2022) Heft 1 . - 015032.
DOI: https://doi.org/10.1088/2632-2153/ac568d
Staacke, Carsten G. ; Huss, Tabea ; Margraf, Johannes T. ; Reuter, Karsten ; Scheurer, Christoph:
Tackling structural complexity in Li₂S-P₂S₅ Solid-State Electrolytes using Machine Learning Potentials.
In: Nanomaterials. Bd. 12 (2022) Heft 17 . - 2950.
DOI: https://doi.org/10.3390/nano12172950
Tackling structural complexity in Li₂S-P₂S₅ Solid-State Electrolytes using Machine Learning Potentials.
In: Nanomaterials. Bd. 12 (2022) Heft 17 . - 2950.
DOI: https://doi.org/10.3390/nano12172950
Staacke, Carsten G. ; Heenen, Hendrik H. ; Scheurer, Christoph ; Csányi, Gábor ; Reuter, Karsten ; Margraf, Johannes T.:
On the role of long-range electrostatics in machine-learned interatomic potentials for complex battery materials.
In: ACS Applied Energy Materials. Bd. 4 (2021) Heft 11 . - S. 12562-12569.
DOI: https://doi.org/10.1021/acsaem.1c02363
On the role of long-range electrostatics in machine-learned interatomic potentials for complex battery materials.
In: ACS Applied Energy Materials. Bd. 4 (2021) Heft 11 . - S. 12562-12569.
DOI: https://doi.org/10.1021/acsaem.1c02363
Stuke, Annika ; Kunkel, Christian ; Golze, Dorothea ; Todorović, Milica ; Margraf, Johannes T. ; Reuter, Karsten ; Rinke, Patrick ; Oberhofer, Harald:
Atomic structures and orbital energies of 61,489 crystal-forming organic molecules.
In: Scientific Data. Bd. 7 (2020) . - 58.
DOI: https://doi.org/10.1038/s41597-020-0385-y
Atomic structures and orbital energies of 61,489 crystal-forming organic molecules.
In: Scientific Data. Bd. 7 (2020) . - 58.
DOI: https://doi.org/10.1038/s41597-020-0385-y
Stocker, Sina ; Csányi, Gábor ; Reuter, Karsten ; Margraf, Johannes T.:
Machine learning in chemical reaction space.
In: Nature Communications. Bd. 11 (2020) . - 5505.
DOI: https://doi.org/10.1038/s41467-020-19267-x
Machine learning in chemical reaction space.
In: Nature Communications. Bd. 11 (2020) . - 5505.
DOI: https://doi.org/10.1038/s41467-020-19267-x
T
Türk, Hanna ; Landini, Elisabetta ; Kunkel, Christian ; Margraf, Johannes T. ; Reuter, Karsten:
Assessing Deep Generative Models in Chemical Composition Space.
In: Chemistry of Materials. Bd. 34 (2022) Heft 21 . - S. 9455-9467.
DOI: https://doi.org/10.1021/acs.chemmater.2c01860
Assessing Deep Generative Models in Chemical Composition Space.
In: Chemistry of Materials. Bd. 34 (2022) Heft 21 . - S. 9455-9467.
DOI: https://doi.org/10.1021/acs.chemmater.2c01860
Timmermann, Jakob ; Lee, Yonghyuk ; Staacke, Carsten G. ; Margraf, Johannes T. ; Scheurer, Christoph ; Reuter, Karsten:
Data-Efficient Iterative Training of Gaussian Approximation Potentials: Application to Surface Structure Determination of Rutile IrO₂ and RuO₂.
In: The Journal of Chemical Physics. Bd. 55 (2021) . - 244107.
DOI: https://doi.org/10.1063/5.0071249
Data-Efficient Iterative Training of Gaussian Approximation Potentials: Application to Surface Structure Determination of Rutile IrO₂ and RuO₂.
In: The Journal of Chemical Physics. Bd. 55 (2021) . - 244107.
DOI: https://doi.org/10.1063/5.0071249
V
Vondrák, Martin ; Reuter, Karsten ; Margraf, Johannes T.:
q-pac: A Python Package for Machine Learned Charge Equilibration Models.
In: The Journal of Chemical Physics. Bd. 159 (2023) . - 054109.
DOI: https://doi.org/10.1063/5.0156290
q-pac: A Python Package for Machine Learned Charge Equilibration Models.
In: The Journal of Chemical Physics. Bd. 159 (2023) . - 054109.
DOI: https://doi.org/10.1063/5.0156290
W
Wengert, Simon ; Csányi, Gábor ; Reuter, Karsten ; Margraf, Johannes T.:
A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-Crystal Screenings.
In: Journal of Chemical Theory and Computation. Bd. 18 (2022) Heft 7 . - S. 4586-4593.
DOI: https://doi.org/10.1021/acs.jctc.2c00343
A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-Crystal Screenings.
In: Journal of Chemical Theory and Computation. Bd. 18 (2022) Heft 7 . - S. 4586-4593.
DOI: https://doi.org/10.1021/acs.jctc.2c00343
Wengert, Simon ; Csányi, Gábor ; Reuter, Karsten ; Margraf, Johannes T.:
Data-Efficient Machine Learning for Molecular Crystal Structure Prediction.
In: Chemical Science. Bd. 12 (2021) Heft 12 . - S. 4536-4546.
DOI: https://doi.org/10.1039/D0SC05765G
Data-Efficient Machine Learning for Molecular Crystal Structure Prediction.
In: Chemical Science. Bd. 12 (2021) Heft 12 . - S. 4536-4546.
DOI: https://doi.org/10.1039/D0SC05765G
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