Publications of PN 3

S. Tovey, C. Holm, and M. Spannowsky, “Generating Reservoir State Descriptions with Random Matrices.” Apr. 2024. doi: arXiv:2404.07278.

S. Tovey, C. Lohrmann, and C. Holm, “Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning.” 2024. doi: https://doi.org/10.48550/arXiv.2404.01999.

C. Lohrmann, C. Holm, and S. S. Datta, “Influence of bacterial swimming and hydrodynamics on infection by phages,” bioRxiv, Jan. 2024, doi: 10.1101/2024.01.15.575727.

M. Fleck, S. Darouich, N. Hansen, and J. Gross, “Transferable Anisotropic Mie Potential Force Field for Alkanediols,” The Journal of Physical Chemistry B, vol. 128, no. 19, Art. no. 19, May 2024, doi: 10.1021/acs.jpcb.4c00962.

M. Pechlaner, W. F. van Gunsteren, L. J. Smith, B. Stankiewicz, L. N. Wirz, and N. Hansen, “Molecular Structure Refinement Based on Residual Dipolar Couplings: A Comparison of the Molecular Rotational-Sampling Method with the Alignment-Tensor Approach,” Journal of Chemical Information and Modeling, vol. 64, no. 12, Art. no. 12, Jun. 2024, doi: 10.1021/acs.jcim.4c00416.

A. Schneider, T. B. Lystbæk, D. Markthaler, N. Hansen, and B. Hauer, “Biocatalytic stereocontrolled head-to-tail cyclizations of unbiased terpenes as a tool in chemoenzymatic synthesis,” Nature Communications, vol. 15, no. 1, Art. no. 1, Jun. 2024, doi: 10.1038/s41467-024-48993-9.

A. Schlaich, J. O. Daldrop, B. Kowalik, M. Kanduč, E. Schneck, and R. R. Netz, “Water Structuring Induces Nonuniversal Hydration Repulsion between Polar Surfaces: Quantitative Comparison between Molecular Simulations, Theory, and Experiments,” Langmuir, vol. 40, no. 15, Art. no. 15, Apr. 2024, doi: 10.1021/acs.langmuir.3c03656.

J. Berberich, D. Fink, and C. Holm, “Robustness of quantum algorithms against coherent control errors,” Physical Review A, vol. 109, no. 1, Art. no. 1, Jan. 2024, doi: 10.1103/PhysRevA.109.012417.

X. Xu, X. Zhang, A. Ruban, S. Schmauder, and B. T. Grabowski, “Accurate complex-stacking-fault Gibbs energy in Ni3Al at high temperatures,” Scripta materialia, vol. 242, p. 115934, 2024, doi: 10.1016/j.scriptamat.2023.115934.

B. Bursik, R. Stierle, A. Schlaich, P. Rehner, and J. Gross, “Viscosities of inhomogeneous systems from generalized entropy scaling,” Physics of Fluids, vol. 36, no. 4, Art. no. 4, Apr. 2024, doi: 10.1063/5.0189902.

H. F. Carvalho, L. Mestrom, U. Hanefeld, and J. Pleiss, “Beyond the Chemical Step: The Role of Substrate Access in Acyltransferase from Mycobacterium smegmatis,” ACS Catal., vol. 14, pp. 10077--10088, Jun. 2024, doi: 10.1021/acscatal.4c00812.

M. Fleck, J. Gross, and N. Hansen, “Multifidelity Gaussian Processes for Predicting Shear Viscosity over Wide Ranges of Liquid State Points Based on Molecular Dynamics Simulations,” Industrial & Engineering Chemistry Research, vol. 63, no. 8, Art. no. 8, 2024, doi: 10.1021/acs.iecr.3c03931.

F. Zills, M. R. Schäfer, S. Tovey, J. Kästner, and C. Holm, “Machine Learning-Driven Investigation of the Structure and Dynamics of the BMIM-BF₄ Room Temperature Ionic Liquid,” Faraday Discussions, 2024, doi: 10.1039/D4FD00025K.

F. Zills, M. R. Schäfer, N. Segreto, J. Kästner, C. Holm, and S. Tovey, “Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly,” The Journal of Physical Chemistry B, Apr. 2024, doi: 10.1021/acs.jpcb.3c07187.

S. Keshav, F. Fritzen, and M. Kabel, “FFT-based homogenization at finite strains using composite boxels (ComBo),” Computational Mechanics, vol. 71, pp. 191–212, Oct. 2023, doi: 10.1007/s00466-022-02232-4.

Molpeceres, G., Zaverkin, V., Furuya, K., Aikawa, Y., and Kästner, J., “Reaction dynamics on amorphous solid water surfaces using interatomic machine-learned potentials - Microscopic energy partition revealed from the P + H → PH reaction,” Astronomy & Astrophysics, vol. 673, p. A51, 2023, doi: 10.1051/0004-6361/202346073.

C. Lohrmann and C. Holm, “Optimal motility strategies for self-propelled agents to explore porous media,” Physical Review B, vol. 108, no. 5, Art. no. 5, Nov. 2023, doi: 10.1103/PhysRevE.108.054401.

S. Gravelle, S. Haber-Pohlmeier, C. Mattea, S. Stapf, C. Holm, and A. Schlaich, “NMR Investigation of Water in Salt Crusts: Insights from Experiments and Molecular Simulations,” Langmuir, vol. 39, no. 22, Art. no. 22, May 2023, doi: 10.1021/acs.langmuir.3c00036.

H. Jäger, A. Schlaich, J. Yang, C. Lian, S. Kondrat, and C. Holm, “A screening of results on the decay length in concentrated electrolytes,” Faraday Discussions, vol. 246, no. 0, Art. no. 0, Aug. 2023, doi: 10.1039/D3FD00043E.

J. Wachlmayr, G. Fläschner, K. Pluhackova, W. Sandtner, C. Siligan, and A. Horner, “Entropic barrier of water permeation through single-file channels,” Communications Chemistry, vol. 6, no. 1, Art. no. 1, Jun. 2023, doi: 10.1038/s42004-023-00919-0.

V. Artemov et al., “The Three-Phase Contact Potential Difference Modulates the Water Surface Charge,” The Journal of Physical Chemistry Letters, vol. 14, no. 20, Art. no. 20, May 2023, doi: 10.1021/acs.jpclett.3c00479.

V. Zaverkin, D. Holzmüller, L. Bonfirraro, and J. Kästner, “Transfer learning for chemically accurate interatomic neural network potentials,” Physical Chemistry Chemical Physics, vol. 25, no. 7, Art. no. 7, 2023, doi: 10.1039/D2CP05793J.

S. Sharba, J. Herb, and F. Fritzen, “Reduced order homogenization of thermoelastic materials with strong temperature dependence and comparison to a machine-learned model,” Archive of Applied Mechanics, vol. 93, no. 7, Art. no. 7, Jul. 2023, doi: 10.1007/s00419-023-02411-6.

J. Yang, S. Kondrat, C. Lian, H. Liu, A. Schlaich, and C. Holm, “Solvent Effects on Structure and Screening in Confined Electrolytes,” Physical  Review Letters, vol. 131, no. 11, Art. no. 11, Sep. 2023, doi: 10.1103/PhysRevLett.131.118201.

X. Xu, X. Zhang, A. Ruban, S. Schmauder, and B. Grabowski, “Strong impact of spin fluctuations on the antiphase boundaries of weak itinerant ferromagnetic Ni3Al,” Acta Materialia, vol. 255, p. 118986, Aug. 2023, doi: 10.1016/j.actamat.2023.118986.

I. Tischler, A. Schlaich, and C. Holm, “Disentanglement of Surface and Confinement Effects for Diene Metathesis in Mesoporous Confinement,” ACS Omega, vol. 9, no. 1, Art. no. 1, Dec. 2023, doi: 10.1021/acsomega.3c06195.

S. Gravelle, D. Beyer, M. Brito, A. Schlaich, and C. Holm, “Assessing the Validity of NMR Relaxation Rates Obtained from Coarse-Grained Simulations of PEG–Water Mixtures,” The Journal of Physical Chemistry B, vol. 127, no. 25, Art. no. 25, Jun. 2023, doi: 10.1021/acs.jpcb.3c01646.

S. Bolik et al., “Lipid bilayer properties potentially contributed to the evolutionary disappearance of betaine lipids in seed plants,” BMC biology, vol. 21, p. 275, 2023, doi: 10.1186/s12915-023-01775-z.

C. Lohrmann and C. Holm, “A novel model for biofilm initiation in porous media flow,” Soft Matter, vol. 19, no. 36, Art. no. 36, 2023, doi: 10.1039/D3SM00575E.

M. Degen et al., “Structural basis of NINJ1-mediated plasma membrane rupture in cell death,” Nature, vol. 618, no. 7967, Art. no. 7967, Jun. 2023, doi: 10.1038/s41586-023-05991-z.

J. Rettberg et al., “Port-Hamiltonian fluid–structure interaction modelling and structure-preserving model order reduction of a classical guitar,” Mathematical and Computer Modelling of Dynamical Systems, vol. 29, no. 1, Art. no. 1, 2023, doi: 10.1080/13873954.2023.2173238.

K. Gubaev, V. Zaverkin, P. Srinivasan, A. I. Duff, J. Kästner, and B. Grabowski, “Performance of two complementary machine-learned potentials in modelling chemically complex systems,” NPJ Computational Materials, vol. 9, p. 129, 2023, doi: 10.1038/s41524-023-01073-w.

C. Lienstromberg, S. Schiffer, and R. Schubert, “A data-driven approach to viscous fluid mechanics: The stationary case,” Arch. Rational Mech. Anal., vol. 247, no. 2, Art. no. 2, 2023, doi: 10.1007/s00205-023-01849-w.

L. Pfitzer, J. Heitkämper, J. Kästner, and R. Peters, “Use of the N–O Bonds in N-Mesyloxyamides and N-Mesyloxyimides To Gain Access to 5-Alkoxy-3,4-dialkyloxazol-2-ones and 3-Hetero-Substituted Succinimides: A Combined Experimental and Theoretical Study,” Synthesis, vol. 55, no. 26, Art. no. 26, 2023, doi: 10.1055/s-0042-1751447.

Á. D. Carral, X. Xu, S. Gravelle, A. YazdanYar, S. Schmauder, and M. Fyta, “Stability of binary precipitates in Cu-Ni-Si-Cr alloys investigated through active learning,” Materials Chemistry and Physics, vol. 306, p. 128053, Sep. 2023, doi: 10.1016/j.matchemphys.2023.128053.

A. Schlaich, D. Jin, L. Bocquet, and B. Coasne, “Electronic screening using a virtual Thomas--Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces,” Nature Materials, vol. 21, no. 2, Art. no. 2, Feb. 2022, doi: 10.1038/s41563-021-01121-0.

K. Pluhackova, V. Schittny, P.-C. Bürkner, C. Siligan, and A. Horner, “Multiple pore lining residues modulate water permeability of GlpF,” Protein Science, vol. 31, no. 10, Art. no. 10, 2022, doi: https://doi.org/10.1002/pro.4431.

M. Pechlaner, W. F. van Gunsteren, N. Hansen, and L. J. Smith, “Molecular dynamics simulation or structure refinement of proteins: are solvent molecules required? A case study using hen lysozyme,” European Biophysics Journal, vol. 51, no. 3, Art. no. 3, Apr. 2022, doi: 10.1007/s00249-022-01593-1.

W. F. van Gunsteren, M. Pechlaner, L. J. Smith, B. Stankiewicz, and N. Hansen, “A Method to Derive Structural Information on Molecules from Residual Dipolar Coupling NMR Data,” The Journal of Physical Chemistry B, vol. 126, no. 21, Art. no. 21, May 2022, doi: 10.1021/acs.jpcb.2c02410.

S. Liese, A. Schlaich, and R. R. Netz, “Dielectric Constant of Aqueous Solutions of Proteins and Organic Polymers from Molecular Dynamics Simulations,” The Journal of Chemical Physics, 2022, doi: 10.1063/5.0089397.

I. Tischler, F. Weik, R. Kaufmann, M. Kuron, R. Weeber, and C. Holm, “A thermalized electrokinetics model including stochastic reactions suitable for multiscale simulations of reaction-advection-diffusion systems,” Journal of Computational Science, vol. 63, p. 101770, 2022, doi: 10.1016/j.jocs.2022.101770.

V. Korn and K. Pluhackova, “Not sorcery after all: Roles of multiple charged residues in membrane insertion of gasdermin-A3,” Frontiers in Cell and Developmental Biology, vol. 10, 2022, doi: 10.3389/fcell.2022.958957.

N. E. R. Zimmermann, G. Guevara-Carrion, J. Vrabec, and N. Hansen, “Predicting and Rationalizing the Soret Coefficient of Binary Lennard-Jones Mixtures in the Liquid State,” Advanced Theory and Simulations, vol. 5, no. 11, Art. no. 11, Jul. 2022, doi: 10.1002/adts.202200311.

D. Markthaler, H. Kraus, and N. Hansen, “Binding free energies for the SAMPL8 CB8 ‘Drugs of Abuse’ challenge from umbrella sampling combined with Hamiltonian replica exchange,” Journal of Computer-Aided Molecular Design, vol. 36, pp. 1–9, 2022, doi: 10.1007/s10822-021-00439-w.

M. Gültig, J. P. Range, B. Schmitz, and J. Pleiss, “Integration of Simulated and Experimentally Determined Thermophysical Properties of Aqueous Mixtures by ThermoML,” Journal of Chemical & Engineering Data, vol. 67, no. 11, Art. no. 11, 2022, doi: 10.1021/acs.jced.2c00391.

D. Markthaler, M. Fleck, B. Stankiewicz, and N. Hansen, “Exploring the Effect of Enhanced Sampling on Protein Stability Prediction,” Journal of Chemical Theory and Computation, vol. 18, no. 4, Art. no. 4, Mar. 2022, doi: 10.1021/acs.jctc.1c01012.

N. Gössweiner-Mohr et al., “The Hidden Intricacies of Aquaporins: Remarkable Details in a Common Structural Scaffold,” Small, vol. 18, no. 31, Art. no. 31, 2022, doi: https://doi.org/10.1002/smll.202202056.

S. Gravelle, C. Holm, and A. Schlaich, “Transport of thin water films: from thermally activated random walks to hydrodynamics,” The Journal of Chemical Physics, 2022, doi: 10.1063/5.0099646.

C. Kessler et al., “Influence of layer slipping on adsorption of light gases in covalent organic frameworks: A combined experimental and computational study,” Microporous and Mesoporous Materials, vol. 336, p. 111796, May 2022, doi: 10.1016/j.micromeso.2022.111796.

V. Zaverkin, D. Holzmüller, R. Schuldt, and J. Kästner, “Predicting properties of periodic systems from cluster data: A case study of liquid water,” The Journal of Chemical Physics, vol. 156, no. 11, Art. no. 11, 2022, doi: 10.1063/5.0078983.

V. Zaverkin, J. Netz, F. Zills, A. Köhn, and J. Kästner, “Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments,” Journal of Chemical Theory and Computation, vol. 18, pp. 1–12, 2022, doi: 10.1021/acs.jctc.1c00853.

S. Nirupama Sriram, E. Polukhov, and M.-A. Keip, “Transient stability analysis of composite hydrogel structures based on a minimization-type variational formulation,” International Journal of Solids and Structures, vol. 230–231, p. 111080, 2021, doi: https://doi.org/10.1016/j.ijsolstr.2021.111080.

L. J. Smith, W. F. van Gunsteren, B. Stankiewicz, and N. Hansen, “On the use of 3J-coupling NMR data to derive structural information on proteins,” Journal of Biomolecular NMR, vol. 75, no. 1, Art. no. 1, Jan. 2021, doi: 10.1007/s10858-020-00355-5.

J. Zeman, S. Kondrat, and C. Holm, “Ionic screening in bulk and under confinement,” The Journal of Chemical Physics, vol. 155, no. 20, Art. no. 20, 2021, doi: 10.1063/5.0069340.

A. Wagner et al., “Permeability Estimation of Regular Porous Structures: A Benchmark for Comparison of Methods,” Transport in Porous Media, vol. 138, no. 1, Art. no. 1, 2021, doi: 10.1007/s11242-021-01586-2.

A. Schlaich, D. Jin, L. Bocquet, and B. Coasne, “Electronic screening using a virtual Thomas--Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces,” Nature Materials, Nov. 2021, doi: 10.1038/s41563-021-01121-0.

H. Carvalho, V. Ferrario, and J. Pleiss, “The molecular mechanism of methanol inhibition in CALB-catalyzed alcoholysis: analyzing molecular dynamics simulations by a Markov state model,” J Chem Theory Comput, vol. 17, pp. 6570–6582, 2021, doi: https://doi.org/10.1021/acs.jctc.1c00559.

E. Polukhov and M.-A. Keip, “Multiscale stability analysis of periodic magnetorheological elastomers,” Mechanics of Materials, vol. 159, p. 103699, 2021, doi: https://doi.org/10.1016/j.mechmat.2020.103699.

K. Szuttor, P. Kreissl, and C. Holm, “A numerical investigation of analyte size effects in nanopore sensing systems,” The Journal of Chemical Physics, vol. 155, no. 13, Art. no. 13, 2021, doi: 10.1063/5.0065085.

D. Markthaler and N. Hansen, “Umbrella sampling and double decoupling data for methanol binding to Candida antarctica lipase B,” Data in Brief, vol. 39, p. 107618, Dec. 2021, doi: 10.1016/j.dib.2021.107618.

D. Born and J. Kästner, “Geometry Optimization in Internal Coordinates Based on Gaussian Process Regression: Comparison of Two Approaches,” Journal of Chemical Theory and Computation, vol. 17, no. 9, Art. no. 9, 2021, doi: 10.1021/acs.jctc.1c00517.

G. Molpeceres, V. Zaverkin, N. Watanabe, and J. Kästner, “Binding energies and sticking coefficients of H₂ on crystalline and amorphous CO ice,” Astronomy & Astrophysics, vol. 648, p. A84, 2021, doi: 10.1051/0004-6361/202040023.

K. Szuttor, F. Weik, J.-N. Grad, and C. Holm, “Modeling the current modulation of bundled DNA structures in nanopores,” The Journal of Chemical Physics, vol. 154, no. 5, Art. no. 5, 2021, doi: 10.1063/5.0038530.

L. J. Smith, W. F. Gunsteren, and N. Hansen, “On the Use of Side-Chain NMR Relaxation Data to Derive Structural and Dynamical Information on Proteins: A Case Study Using Hen Lysozyme,” ChemBioChem, vol. 22, no. 6, Art. no. 6, Dec. 2021, doi: 10.1002/cbic.202000674.

J. M. Riede, C. Holm, S. Schmitt, and D. F. B. Haeufle, “The control effort to steer self-propelled microswimmers depends on their morphology: comparing symmetric spherical versus asymmetric              L              -shaped particles,” Royal Society Open Science, vol. 8, no. 9, Art. no. 9, Sep. 2021, doi: 10.1098/rsos.201839.

M. Kuron, C. Stewart, J. de Graaf, and C. Holm, “An extensible lattice Boltzmann method for viscoelastic flows: complex and moving boundaries in Oldroyd-B fluids,” The European Physical Journal E, vol. 44, no. 1, Art. no. 1, 2021, doi: 10.1140/epje/s10189-020-00005-6.

X. Xu, P. Binkele, W. Verestek, and S. Schmauder, “Molecular Dynamics Simulation of High-Temperature Creep Behavior of Nickel Polycrystalline Nanopillars,” Molecules, vol. 26, no. 9, Art. no. 9, Apr. 2021, doi: 10.3390/molecules26092606.

X. Xu, J. Range, G. Gygli, and J. Pleiss, “Analysis of Thermophysical Properties of Deep Eutectic Solvents by Data Integration,” Journal of Chemical & Engineering Data, vol. 65, pp. 1172–1179, 2020, doi: https://doi.org/10.1021/acs.jced.9b00555.

N. Hansen et al., “A Suite of Advanced Tutorials for the GROMOS Biomolecular Simulation Software Article v1.0,” Living Journal of Computational Molecular Science, vol. 2, no. 1, Art. no. 1, 2020, doi: 10.33011/livecoms.2.1.18552.

J. Zeman, S. Kondrat, and C. Holm, “Bulk ionic screening lengths from extremely large-scale molecular dynamics simulations,” Chemical Communications, vol. 56, no. 100, Art. no. 100, 2020, doi: 10.1039/D0CC05023G.

S. Tovey et al., “DFT accurate interatomic potential for molten NaCl from machine learning,” The Journal of Physical Chemistry C, vol. 124, no. 47, Art. no. 47, 2020, doi: 10.1021/acs.jpcc.0c08870.

G. Molpeceres, V. Zaverkin, and J. Kästner, “Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – I. adsorption and desorption,” Mon. Not. R. Astron. Soc., vol. 499, pp. 1373–1384, 2020, doi: 10.1093/mnras/staa2891.

J. Gebhardt, M. Kiesel, S. Riniker, and N. Hansen, “Combining Molecular Dynamics and Machine Learning to Predict Self-Solvation Free Energies and Limiting Activity Coefficients,” Journal of Chemical Information and Modeling, vol. 60, no. 11, Art. no. 11, Aug. 2020, doi: 10.1021/acs.jcim.0c00479.

G. Sivaraman et al., “Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide,” npj Computational Materials, vol. 6, no. 1, Art. no. 1, Jul. 2020, doi: 10.1038/s41524-020-00367-7.

V. Zaverkin and J. Kästner, “Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials,” Journal of Chemical Theory and Computation, vol. 16, pp. 5410–5421, 2020, doi: 10.1021/acs.jctc.0c00347.

L. T. K. Nguyen, M. Rambausek, and M.-A. Keip, “Variational framework for distance-minimizing method in data-driven computational mechanics,” Computer Methods in Applied Mechanics and Engineering, vol. 365, p. 112898, 2020, doi: https://doi.org/10.1016/j.cma.2020.112898.

K. Breitsprecher et al., “How to speed up ion transport in nanopores,” Nature Communications, vol. 11, no. 1, Art. no. 1, Nov. 2020, doi: 10.1038/s41467-020-19903-6.

G. Gygli, X. Xu, and J. Pleiss, “Meta-analysis of viscosity of aqueous deep eutectic solvents and their components,” Scientific Reports, vol. 10, pp. 21395–21395, 2020.

A. Denzel and J. Kästner, “Hessian Matrix Update Scheme for Transition State Search Based on Gaussian Process Regression,” Journal of Chemical Theory and Computation, vol. 16, no. 8, Art. no. 8, Jul. 2020, doi: 10.1021/acs.jctc.0c00348.

E. Polukhov and M.-A. Keip, “Computational homogenization of transient chemo-mechanical processes based on a variational minimization principle,” Advanced Modeling and Simulation in Engineering Sciences, vol. 7, no. 1, Art. no. 1, Jul. 2020, doi: 10.1186/s40323-020-00161-6.

J. Zeman, C. Holm, and J. Smiatek, “The Effect of Small Organic Cosolutes on Water Structure and Dynamics,” Journal of Chemical & Engineering Data, vol. 65, no. 3, Art. no. 3, Aug. 2019, doi: 10.1021/acs.jced.9b00577.

F. S. Göküzüm, L. T. K. Nguyen, and M.-A. Keip, “An Artificial Neural Network Based Solution Scheme for Periodic Computational Homogenization of Electrostatic Problems,” Mathematical and Computational Applications, vol. 24, no. 2, Art. no. 2, Apr. 2019, doi: 10.3390/mca24020040.

A. Denzel, B. Haasdonk, and J. Kästner, “Gaussian Process Regression for Minimum Energy Path Optimization and Transition State Search,” The Journal of Physical Chemistry A, vol. 123, no. 44, Art. no. 44, 2019, doi: 10.1021/acs.jpca.9b08239.

R. Roddan et al., “The acceptance and kinetic resolution of alpha-methyl substituted aldehydes by norcoclaurine synthases.,” ACS Catalysis, vol. 9, pp. 9640–9649, 2019.

A. Denzel and J. Kästner, “Gaussian Process Regression for Transition State Search,” Journal of Chemical Theory and Computation, vol. 14, no. 11, Art. no. 11, 2018, doi: 10.1021/acs.jctc.8b00708.

A. Denzel and J. Kästner, “Gaussian process regression for geometry optimization,” Journal of Chemical Physics, vol. 148, no. 9, Art. no. 9, 2018, doi: 10.1063/1.5017103.

D. Holzmüller, “Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression.” 2021. doi: 10.18419/darus-1771.

J. Rettberg et al., “Replication Data for: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar.” 2023. doi: 10.18419/darus-3248.

M. Degen et al., “Supplementary material for ‘Structural basis for ninjurin-1 mediated plasma membrane rupture in lytic cell death.’” 2023. doi: 10.18419/darus-3373.

J. Wachlmayr, G. Fläschner, K. Pluhackova, W. Sandtner, C. Siligan, and A. Horner, “Supplementary Material for ‘Entropic barrier of water permeation through single-file channels.’” 2023. doi: 10.18419/darus-3390.

X. Xu, “Replication Data for: Strong impact of spin fluctuations on the antiphase boundaries of weak itinerant ferromagnetic Ni3Al.” 2023. doi: 10.18419/darus-3579.