Publications of PN 3

  1. 2022

    1. 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.
  2. 2021

    1. 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.
    2. 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: https://doi.org/10.1002/anie.202012796.
    3. 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.
    4. 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.
    5. 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.
    6. 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: https://doi.org/10.1016/j.elecom.2021.107137.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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. n/a, no. n/a, Art. no. n/a, 2021, doi: 10.1002/nme.6869.
    12. 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.
    13. 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.
  3. 2020

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. M. Fernández and F. Fritzen, “On the generation of periodic discrete structures with identical two-point correlation,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 476, no. 2242, Art. no. 2242, 2020, doi: 10.1098/rspa.2020.0568.
    12. 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, p. 18, 2020, [Online]. Available: https://doi.org/10.1155/2020/9091387
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. T. Bauer, P. Buchholz, and J. Pleiss, “The modular structure of α/β-hydrolases.,” FEBS J, vol. 287, pp. 1035–1053, 2020.
  4. 2019

    1. 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, no. 3, Art. no. 3, Sep. 2019, doi: 10.1021/acs.jced.9b00555.
    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 for Periodic Computational Homogenization of Electrostatic Problems,” Mathematical and Computational Applications, vol. 24, no. 2, Art. no. 2, Apr. 2019, doi: 10.3390/mca24020040.
    4. 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.
    5. 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.
    6. V. Ferrario, M. Fischer, Y. Zhu, and J. Pleiss, “Modelling of substrate access and substrate binding to cephalosporin acylases,” Scientific Reports, vol. 9, no. 1, Art. no. 1, Aug. 2019, doi: 10.1038/s41598-019-48849-z.
    7. A. Denzel, B. Haasdonk, and J. Kästner, “Gaussian Process Regression for Minimum Energy Path Optimization and Transition State Search,” J. Phys. Chem. A, vol. 123, no. 44, Art. no. 44, 2019, doi: 10.1021/acs.jpca.9b08239.
    8. A. D. Carral, C. S. Sarap, K. Liu, A. Radenovic, and M. Fyta, “2D MoS2 nanopores: ionic current blockade height for clustering DNA events,” 2D Materials, vol. 6, no. 4, Art. no. 4, 2019.
    9. J. Baz, C. Held, J. Pleiss, and N. Hansen, “Thermophysical properties of glyceline–water mixtures investigated by molecular modelling,” Phys. Chem. Chem. Phys., vol. 21, no. 12, Art. no. 12, 2019, doi: 10.1039/C9CP00036D.

Project Network Coordinators

This image shows Manfred Bischoff

Manfred Bischoff

Prof. Dr.-Ing. habil.

[Photo: SimTech/Max Kovalenko]

Christian Holm

Prof. Dr.
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