I. Tischler, A. Schlaich, and C. Holm. Software, (2023)Related to: Ingo Tischler, Alexander Schlaich, Christian Holm. Disentanglement of Surface and Confinement Effects for Diene Metathesis in Mesoporous Confinement. ACS Omega 2023. doi: 10.1021/acsomega.3c06195.
S. Gravelle, D. Beyer, M. Brito, A. Schlaich, and C. Holm. Software, (2023)Related to: Simon Gravelle, David Beyer, Mariano Brito, Alexander Schlaich, Christian Holm: Reconstruction of NMR Relaxation Rates from Coarse-Grained Polymer Simulations, ChemRxiv, 2022. Preprint. doi: 10.26434/chemrxiv-2022-f90tv-v2.
S. Gravelle, C. Holm, and A. Schlaich. Software, (2023)Related to: Simon Gravelle, Sabina Haber-Pohlmeier, Carlos Mattea, Siegfried Stapf, Christian Holm and Alexander Schlaich: NMR Investigation of Water in Salt Crusts: Insights from Experiments and Molecular Simulations, ChemRxiv, 2023. doi: 10.26434/chemrxiv-2023-6dml7.
I. Tischler, C. Holm, and A. Schlaich. Software, (2022)Related to: Tischler, I., Schlaich, A., Holm, C., The Presence of a Wall Enhances the Probability for Ring-Closing Metathesis: Insights from Classical Polymer Theory and Atomistic Simulations. Macromol. Theory Simul. 2021, 30, 2000076. doi: 10.1002/mats.202000076.
R. Herkert. Software, (2023)Related to: R. Herkert, P. Buchfink, B. Haasdonk, J. Rettberg, J. Fehr: Randomized Symplectic Model Order Reduction for Hamiltonian Systemsm 2023. arXiv: 2303.04036.
M. Alkämper, and J. Magiera. Software, (2022)Related to: M. Alkämper, J. M. Magiera and C. Rohde, “An Interface Preserving Moving Mesh in Multiple Space Dimensions” (2021), submitted. arXiv: 2112.11956.
R. Herkert. Software, (2024)Related to: R. Herkert, P. Buchfink, T. Wenzel, B. Haasdonk, P. Toktaliev, O. Iliev (2024), "Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data". arXiv: 2405.19170.
P. Stärk, and A. Schlaich. Software, (2024)Related to: Artemov, V.; Frank, L.; Doronin, R.; Stärk, P.; Schlaich, A.; Andreev, A.; Leisner, T.; Radenovic, A.; Kiselev, A. The Three-Phase Contact Potential Difference Modulates the Water Surface Charge. J. Phys. Chem. Lett. 2023, 14, 4796-4802. doi: 10.1021/acs.jpclett.3c00479.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2022)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022. arXiv: 2203.09410.
D. Holzmüller. Software, (2021)Related to: David Holzmüller. On the Universality of the Double Descent Peak in Ridgeless Regression. International Conference on Learning Representations, 2021. arXiv: 2010.01851.
J. Rettberg, D. Wittwar, and R. Herkert. Software, (2023)Related to: Rettberg, J.; Wittwar, D.; Buchfink, P.; Brauchler, A.; Ziegler, P.; Fehr, J.; Haasdonk, B.: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar. Mathematical and Computer Modelling of Dynamical Systems, 2023, Vol. 29, No. 1, 116-148. doi: 10.1080/13873954.2023.2173238.
C. Homs Pons, and R. Lautenschlager. Software, (2024)Related to: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models, submitted to GAMM Mitteilungen.
V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner. Software, (2021)Related to: V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” J. Chem. Theory Comput. 17, 6658–6670 (2021). doi: 10.1021/acs.jctc.1c00527.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2022)Related to: David, Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2022. arXiv: 2203.09410.
D. Holzmüller. Software, (2022)Related to: David Holzmüller and Dirk Pflüger. Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework. Sparse Grids and Applications - Munich 2018 (2021). doi: 10.1007/978-3-030-81362-8_4.
R. Herkert. Software, (2024)Related to: R. Herkert, P. Buchfink, B. Haasdonk, J. Rettberg, J. Fehr. (2024), "Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems". arXiv: 2405.10465.
D. Holzmüller, V. Zaverkin, J. Kästner, and I. Steinwart. Software, (2023)Related to: David Holzmüller, Viktor Zaverkin, Johannes Kästner, and Ingo Steinwart. A Framework and Benchmark for Deep Batch Active Learning for Regression, 2023. arXiv: 2203.09410.
S. Gravelle, C. Holm, and A. Schlaich. Software, (2023)Related to: Simon Gravelle, Sabina Haber-Pohlmeier, Carlos Mattea, Siegfried Stapf, Christian Holm and Alexander Schlaich: NMR Investigation of Water in Salt Crusts: Insights from Experiments and Molecular Simulations, ChemRxiv, 2023. doi: 10.26434/chemrxiv-2023-6dml7.
V. Zaverkin, D. Holzmüller, L. Bonfirraro, and J. Kästner. Dataset, (2023)Related to: Viktor Zaverkin, David Holzmüller, Luca Bonfirraro, Johannes Kästner. Transfer learning for chemically accurate interatomic neural network potentials, Phys. Chem. Chem. Phys., 2023, 25, 5383-5396. doi: 10.1039/D2CP05793J.
J. Yang, S. Kondrat, C. Lian, H. Liu, A. Schlaich, and C. Holm. Dataset, (2023)Related to: J. Yang, S. Kondrat, C. Lian, H. Liu, A. Schlaich, und C. Holm, „Solvent Effects on Structure and Screening in Confined Electrolytes“, Phys. Rev. Lett., Bd. 131, Nr. 11, 118201, Sep. 2023. doi: 10.1103/PhysRevLett.131.118201.
F. Huber, P. Bürkner, D. Göddeke, and M. Schulte. Dataset, (2023)Related to: Huber, Felix; Bürkner, Paul-Christian; Göddeke, Dominik; Schulte, MiriamKnowledge-Based Modeling of Simulation Behavior for Bayesian OptimizationComputational Mechanics (submitted).
J. Finkbeiner, S. Tovey, and C. Holm. Dataset, (2024)Related to: Jan Finkbeiner, Samuel Tovey, Christian Holm: Generating Minimal Training Sets for Machine Learned Potentials (2023). arXiv: 2309.03840.
A. Schlaich. Dataset, (2021)Related to: Schlaich, A., Jin, D., Bocquet, L. & Coasne, B. (2021). Wetting transition of ionic liquids at metal surfaces: A computational molecular approach to electronic screening using a virtual Thomas-Fermi fluid. arXiv: 2002.11526.
J. Körber, and J. Heiler. Dataset, (2024)Related to: J. Körber, J. Heiler et al., Fluorescence enhancement of single V2 centers in a 4H-SiC cavity antenna (preprint). arXiv: 2406.08208.
R. Herkert. Software, (2024)Related to: R. Herkert, P. Buchfink, T. Wenzel, B. Haasdonk, P. Toktaliev, O. Iliev (2024), "Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data". arXiv: 2405.19170.
P. Stärk, and A. Schlaich. Software, (2024)Related to: Artemov, V.; Frank, L.; Doronin, R.; Stärk, P.; Schlaich, A.; Andreev, A.; Leisner, T.; Radenovic, A.; Kiselev, A. The Three-Phase Contact Potential Difference Modulates the Water Surface Charge. J. Phys. Chem. Lett. 2023, 14, 4796-4802. doi: 10.1021/acs.jpclett.3c00479.
D. Liu. Dataset, (2024)Related to: Di Liu, Florian Kaiser, Vladislav Bushmakin, Erik Hesselmeier, Timo Steidl, Takeshi Ohshima, Nguyen Tien Son, Jawad Ul-Hassan, Öney O. Soykal, Jörg Wrachtrup (2023). The silicon vacancy centers in SiC: determination of intrinsic spin dynamics for integrated quantum photonics. arXiv preprint. arXiv: 2307.13648.
G. Kemlin, T. Carvalho Corso, B. Stamm, and C. Melcher. Software, (2024)Related to: T. Carvalho Corso, G. Kemlin, C. Melcher and B. Stamm. Numerical simulation of the Gross-Pitaevskii equation via vortex tracking. Preprint (2024). arXiv: 2404.02133.
R. Herkert. Software, (2024)Related to: R. Herkert, P. Buchfink, B. Haasdonk, J. Rettberg, J. Fehr. (2024), "Error Analysis of Randomized Symplectic Model Order Reduction for Hamiltonian systems". arXiv: 2405.10465.
N. Makki, N. Lang, and H. Büchler. Dataset, (2024)Related to: Makki, Nastasia, Lang, Nicolai & Büchler, Hans Peter (2024). Absorbing state phase transition with Clifford circuits. Phys. Rev. Res. 6, 013278. doi: 10.1103/PhysRevResearch.6.013278.