Publications of PN 3

  1. 2023

    1. V. Zaverkin, D. Holzmüller, L. Bonfirraro, and J. Kästner, “Transfer learning for chemically accurate interatomic neural network potentials,” Phys. Chem. Chem. Phys., vol. 25, no. 7, Art. no. 7, 2023, doi: 10.1039/D2CP05793J.
    2. J. Yang, S. Kondrat, C. Lian, H. Liu, A. Schlaich, and C. Holm, “Solvent Effects on Structure and Screening in Confined Electrolytes,” Phys. Rev. Lett., vol. 131, no. 11, Art. no. 11, Sep. 2023, doi: 10.1103/PhysRevLett.131.118201.
    3. 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.
    4. 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.
    5. S. Tovey et al., “Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning.” 2023. doi:
    6. S. Tovey, F. Zills, F. Torres-Herrador, C. Lohrmann, M. Brückner, and C. Holm, “MDSuite: comprehensive post-processing tool for particle simulations,” Journal of Cheminformatics, vol. 15, no. 1, Art. no. 1, Feb. 2023, doi: 10.1186/s13321-023-00687-y.
    7. S. Tovey, S. Krippendorf, K. Nikolaou, and C. Holm, “Towards a Phenomenological Understanding of Neural Networks: Data.” 2023. doi:
    8. 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.
    9. 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.
    10. M. Schneider, D. Born, J. Kästner, and G. Rauhut, “Positioning of grid points for spanning potential energy surfaces–How much effort is really needed?,” J. Chem. Phys., vol. 158, no. 14, Art. no. 14, 2023, doi: 10.1063/5.0146020.
    11. A. Schlaich, S. Tyagi, S. Kesselheim, M. Sega, and C. Holm, “Renormalized charge and dielectric effects in colloidal interactions: a numerical solution of the nonlinear Poisson--Boltzmann equation for unknown boundary conditions,” The European Physical Journal E, vol. 46, no. 9, Art. no. 9, Sep. 2023, doi: 10.1140/epje/s10189-023-00334-2.
    12. J. Rettberg, D. Wittwar, P. Buchfink, R. Herkert, J. Fehr, and B. Haasdonk, “Improved a posteriori Error Bounds for Reduced port-Hamiltonian Systems.” 2023. doi:
    13. 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.
    14. 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.
    15. L. Qiao, K. Szuttor, C. Holm, and G. W. Slater, “Ratcheting Charged Polymers through Symmetric Nanopores Using Pulsed Fields: Designing a Low Pass Filter for Concentrating Polyelectrolytes,” Nano Letters, vol. 23, no. 4, Art. no. 4, 2023, doi:
    16. 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.
    17. T. Paul, “Artificial Intelligence Based Evaluation of Protein Quality: Evaluation of Backmapped Proteins,” 2023.
    18. 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,” Astron. Astrophys., vol. 673, p. A51, 2023, doi: 10.1051/0004-6361/202346073.
    19. C. Lohrmann and C. Holm, “Optimal motility strategies for self-propelled agents to explore porous media,” Phys. Rev. E, vol. 108, no. 5, Art. no. 5, Nov. 2023, doi: 10.1103/PhysRevE.108.054401.
    20. 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.
    21. J. Lißner and F. Fritzen, “Double U‐Net: Improved multiscale modeling via fully convolutional neural networks,” Proceedings in Applied Mathematics & Mechanics (PAMM), Sep. 2023, doi: 10.1002/pamm.202300205.
    22. 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.
    23. 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 Discuss., vol. 246, no. 0, Art. no. 0, Aug. 2023, doi: 10.1039/D3FD00043E.
    24. R. R. Herkert, P. Buchfink, B. Haasdonk, J. Rettberg, and J. C. Fehr, “Randomized Symplectic Model Order Reduction for Hamiltonian Systems,” pp. 1–8, 2023, doi: 10.48550/arXiv.2303.04036.
    25. 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 Comput. Mater., vol. 9, p. 129, 2023, doi: 10.1038/s41524-023-01073-w.
    26. 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.
    27. 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,” Jun. 2023, doi: 10.26434/chemrxiv-2022-f90tv-v4.
    28. W. Gaßner, “PIP2 lipids allosterically modulate the stability and dynamics of the β2-adrenergic receptor/β-arrestin2 complex -Insights from molecular dynamics simulations,” University of Stuttgart, Germany, 2023.
    29. J. Finkbeiner, S. Tovey, and C. Holm, “Generating Minimal Training Sets for Machine Learned Potentials.” 2023. doi:
    30. 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.
    31. S. Bolik et al., “The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants.,” bioRxiv, 2023, doi: 10.1101/2023.01.24.525350.
    32. D. Beyer, P. Koss\fiovan, and C. Holm, “Explaining Giant Apparent $pK_a$ Shifts in Weak Polyelectrolyte Brushes,” Phys. Rev. Lett., vol. 131, no. 16, Art. no. 16, Oct. 2023, doi: 10.1103/PhysRevLett.131.168101.
    33. J. Berberich, D. Fink, D. Pranjić, C. Tutschku, and C. Holm, “Training robust and generalizable quantum models.” 2023. doi:
    34. J. Berberich, D. Fink, and C. Holm, “Robustness of quantum algorithms against coherent control errors.” 2023. doi:
    35. 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.
  2. 2022

    1. 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.
    2. X. Zhang, S. V. Divinski, and B. Grabowski, “Ab initio prediction of vacancy energetics in HCP Al-Hf-Sc-Ti-Zr high entropy alloys and the subsystems,” Acta Materialia, vol. 227, p. 117677, Apr. 2022, doi: 10.1016/j.actamat.2022.117677.
    3. V. Zaverkin, J. Netz, F. Zills, A. Köhn, and J. Kästner, “Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments,” J. Chem. Theory Comput., vol. 18, pp. 1–12, 2022, doi: 10.1021/acs.jctc.1c00853.
    4. 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.
    5. 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.
    6. 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.
    7. J. Rettberg et al., “Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar,” pp. 1–27, 2022, doi: 10.48550/arXiv.2203.10061.
    8. 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:
    9. 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.
    10. I. Novikov, B. Grabowski, F. Körmann, and A. Shapeev, “Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe,” npj Computational Materials, vol. 8, no. 1, Art. no. 1, Jan. 2022, doi: 10.1038/s41524-022-00696-9.
    11. Molpeceres, G., Kästner, J., Herrero, V. J., Peláez, R. J., and Maté, B., “Desorption of organic molecules from interstellar ices, combining experiments and computer simulations: Acetaldehyde as a case study,” Astron. Astrophys., vol. 664, p. A169, 2022, doi: 10.1051/0004-6361/202243489.
    12. Molpeceres, G. et al., “Hydrogen abstraction reactions in formic and thioformic acid isomers by hydrogen and deuterium atoms,” Astron. Astrophys., vol. 663, p. A41, 2022, doi: 10.1051/0004-6361/202243366.
    13. 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.
    14. 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.
    15. 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.
    16. J. S. Lee, W.-S. Ko, and B. Grabowski, “Atomistic simulations of the deformation behavior of an Nb nanowire embedded in a NiTi shape memory alloy,” Acta Materialia, vol. 228, p. 117764, Apr. 2022, doi: 10.1016/j.actamat.2022.117764.
    17. 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.
    18. 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.
    19. 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.
    20. 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:
    21. 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.
    22. M. Fernández, F. Fritzen, and O. Weeger, “Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials,” International Journal for Numerical Methods in Engineering, vol. 123, no. 2, Art. no. 2, 2022, doi: 10.1002/nme.6869.
  3. 2021

    1. 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.
    2. 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.
    3. 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.
    4. M. Titze, J. Heitkämper, T. Junge, J. Kästner, and R. Peters, “Highly Active Cooperative Lewis Acid—Ammonium Salt Catalyst for the Enantioselective Hydroboration of Ketones,” Angew. Chem. Int. Ed., vol. 60, no. 10, Art. no. 10, 2021, doi:
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. L. Oberer, A. D. Carral, and M. Fyta, “Simple Classification of RNA Sequences of Respiratory-Related Coronaviruses,” ACS Omega, Jul. 2021, doi: 10.1021/acsomega.1c01625.
    12. S. Murugan, S. V. Klostermann, W. Frey, J. Kästner, and M. R. Buchmeiser, “A sodium bis(perfluoropinacol) borate-based electrolyte for stable, high-performance room temperature sodium-sulfur batteries based on sulfurized poly(acrylonitrile),” Electrochem. commun., vol. 132, p. 107137, 2021, doi:
    13. G. Molpeceres, V. Zaverkin, N. Watanabe, and J. Kästner, “Binding energies and sticking coefficients of H₂ on crystalline and amorphous CO ice,” Astron. Astrophys., vol. 648, p. A84, 2021, doi: 10.1051/0004-6361/202040023.
    14. G. Molpeceres et al., “Carbon Atom Reactivity with Amorphous Solid Water: H₂O-Catalyzed Formation of H₂CO,” J. Phys. Chem. Lett., vol. 12, no. 44, Art. no. 44, 2021, doi: 10.1021/acs.jpclett.1c02760.
    15. G. Molpeceres and J. Kästner, “Computational Study of the Hydrogenation Sequence of the Phosphorous Atom on Interstellar Dust Grains,” Astrophys. J., vol. 910, p. 55, 2021, doi: 10.3847/1538-4357/abe38c.
    16. A. M. Miksch, A. Riffelt, R. Oliveira, J. Kästner, and G. Molpeceres, “Hydrogenation of small aromatic heterocycles at low temperatures,” Mon. Not. R. Astron. Soc., vol. 505, no. 3, Art. no. 3, 2021, doi: 10.1093/mnras/stab1514.
    17. 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.
    18. 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,” 2021, doi: 10.1140/epje/s10189-020-00005-6.
    19. S. T. Emmerling et al., “Interlayer Interactions as Design Tool for Large-Pore COFs,” J. Am. Chem. Soc., vol. 143, no. 38, Art. no. 38, 2021, doi: 10.1021/jacs.1c06518.
    20. 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:
    21. D. Born and J. Kästner, “Geometry Optimization in Internal Coordinates Based on Gaussian Process Regression: Comparison of Two Approaches,” J. Chem. Theory Comput., vol. 17, no. 9, Art. no. 9, 2021, doi: 10.1021/acs.jctc.1c00517.
    22. D. Born and J. Kästner, “Geometry Optimization in Internal Coordinates Based on Gaussian Process Regression: Comparison of Two Approaches,” J. Chem. Theory Comput., vol. 17, no. 9, Art. no. 9, 2021, doi: 10.1021/acs.jctc.1c00517.
  4. 2020

    1. J. Zeman, S. Kondrat, and C. Holm, “Bulk ionic screening lengths from extremely large-scale molecular dynamics simulations,” Chem. Commun., vol. 56, no. 100, Art. no. 100, 2020, doi: 10.1039/D0CC05023G.
    2. V. Zaverkin and J. Kästner, “Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials,” J. Chem. Theory Comput., vol. 16, pp. 5410–5421, 2020, doi: 10.1021/acs.jctc.0c00347.
    3. 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:
    4. 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.
    5. P. Stockinger, S. Roth, M. Müller, and J. Pleiss, “Systematic evaluation of imine-reducing enzymes: Common principles in imine reductases, β-hydroxyacid dehydrogenases, and short-chain dehydrogenases/reductases,” ChemBioChem, vol. 21, pp. 2689–2695, 2020.
    6. 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.
    7. 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, Jun. 2020, doi: 10.1016/j.cma.2020.112898.
    8. 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.
    9. M. Mangiagalli et al., “Diverse effects of aqueous polar co-solvents on Candida antarctica lipase B.,” Int J Biol Macromol, vol. 150, pp. 930–940, 2020.
    10. 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.
    11. G. Gygli and J. Pleiss, “Simulation foundry: Automated and FAIR molecular modeling,” Journal of chemical information and modeling, vol. 60, no. 4, Art. no. 4, 2020.
    12. G. Gygli, X. Xu, and J. Pleiss, “Meta-analysis of viscosity of aqueous deep eutectic solvents and their components,” Sci Rep, vol. 10, pp. 21395–21395, 2020.
    13. 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.
    14. M. Fischer, G. Bauer, and J. Gross, “Transferable Anisotropic United-Atom Mie (TAMie) Force Field: Transport Properties from Equilibrium Molecular Dynamic Simulations,” Industrial & Engineering Chemistry Research, vol. 59, no. 18, Art. no. 18, 2020.
    15. M. Fernández, S. Rezaei, J. R. Mianroodi, F. Fritzen, and S. Reese, “Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior,” Advanced Modeling and Simulation in Engineering Sciences, vol. 7, no. 1, Art. no. 1, Jan. 2020, doi: 10.1186/s40323-019-0138-7.
    16. M. Fernández and F. Fritzen, “Construction of a class of sharp Löwner majorants for a set of symmetric matrices,” Journal of Applied Mathematics, vol. 2020, pp. 1–18, 2020, doi: 10.1155/2020/9091387.
    17. M. Fernandez and F. Fritzen, “On the generation of periodic discrete structures with identical two-point correlation,” Proceedings of the Royal Society A, vol. 476, no. 2242, Art. no. 2242, 2020.
    18. I. Eisenkolb et al., “Modeling of biocatalytic reactions: A workflow for model calibration, selection and validation using Bayesian statistics,” AIChE J, vol. 66, p. e16866, 2020.
    19. A. Denzel and J. Kästner, “Hessian Matrix Update Scheme for Transition State Search Based on Gaussian Process Regression,” J. Chem. Theory Comput., vol. 16, pp. 5083–5089, 2020, doi: 10.1021/acs.jctc.0c00348.
    20. A. M. Cooper, J. Kästner, A. Urban, and N. Artrith, “Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide,” npj Computational Materials, vol. 6, no. 1, Art. no. 1, 2020.
    21. 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.
    22. T. Bauer, P. Buchholz, and J. Pleiss, “The modular structure of α/β-hydrolases.,” FEBS J, vol. 287, pp. 1035–1053, 2020.
  5. 2019

    1. 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.
    2. R. Roddan et al., “The acceptance and kinetic resolution of alpha-methyl substituted aldehydes by norcoclaurine synthases.,” ACS Catal, vol. 9, pp. 9640–9649, 2019.
    3. F. S. Göküzüm, L. T. K. Nguyen, and M.-A. Keip, “An Artificial Neural Network based Solution Scheme to periodic Homogenization,” PAMM, vol. 19, no. 1, Art. no. 1, Nov. 2019, doi: 10.1002/pamm.201900271.
    4. B. Grabowski et al., “Ab initio vibrational free energies including anharmonicity for multicomponent alloys,” npj Computational Materials, vol. 5, no. 1, Art. no. 1, 2019.
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Project Network Coordinators

This image shows Manfred Bischoff

Manfred Bischoff

Prof. Dr.-Ing. habil.

Structural Mechanics

[Photo: SimTech/Max Kovalenko]

Christian Holm

Prof. Dr.

Computational Physics

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