This image shows Christian Holm

Christian Holm

Prof. Dr.

Head of Institute
Institute for Computational Physics

Contact

+49 711 685 63701
 +49 711 685 53701

Allmandring 3
70569 Stuttgart
Deutschland
Room: 1.046

Office Hours

Mo. 13:15 - 14:00

Subject

My scientific interests are especially the study of complex charged and magnetic soft matter by means of computer simulations, and the development of simple theoretical models to describe them. More precisely I am currently working on the solution properties and association behavior of flexible and semi-flexible polyelectrolytes in various solvents and under various salt concentrations and salt types. In addition I am interested in the effective pair interactions of charged colloidal particles and their phase behavior. This includes simple DNA models, and DNA protein interactions, as well as developing coarse grained models for DNA-Histone complexes. We also investigate in depth polyelectrolyte hydrogels and magnetically interacting ferrogels, as well as pure ferrofluids, where special attention is given to the structure of the solution and the magnetic response functions. Another interest is the applicability of mean-field models for the description of models with long range interactions, and possible improvements beyond the mean-field approach. This include local density functional methods based on the Poisson-Boltzmann functional, as well as strong coupling theories such as Wigner-crystal methods. In addition I am interested in the development of fast methods for the computation of long range interactions. These include pure Coulomb as well as dipolar interactions in various geometries (3D-1D), and under various boundary conditions. And least but not last, we are interested in developing fast methods to deal with fluid-structure couplings using various coupling schemes of particles to a lattice-boltzmann fluid. These can be charged fluids, as well as fluids that undergo reactions at boundaries, such as needed for active Janus-Colloids,or for catalytic particles. We also are interested to apply machine learning algorithms for the development of force-fields with almost DFT precision.

  1. 2024

    1. Zills, F., Schäfer, M. R., Segreto, N., Kästner, J., Holm, C., & Tovey, S. (2024). Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly. The Journal of Physical Chemistry B. https://doi.org/10.1021/acs.jpcb.3c07187
    2. Tovey, S., Holm, C., & Spannowsky, M. (2024). Generating Reservoir State Descriptions with Random Matrices. https://doi.org/arXiv:2404.07278
    3. Brito, M. E., & Holm, C. (2024). Modelling microgel swelling: Influence of chain finite extensibility. https://doi.org/10.26434/chemrxiv-2024-h8q4c
    4. Elijošius, R., Zills, F., Batatia, I., Norwood, S. W., Kovács, D. P., Holm, C., & Csányi, G. (2024). Zero Shot Molecular Generation via Similarity Kernels. https://doi.org/10.48550/arXiv.2402.08708
    5. Berberich, J., Fink, D., & Holm, C. (2024). Robustness of quantum algorithms against coherent control errors. Phys. Rev. A, 109(1), Article 1. https://doi.org/10.1103/PhysRevA.109.012417
    6. Zills, F., Schäfer, M., Tovey, S., Kästner, J., & Holm, C. (2024). ZnTrack -- Data as Code. https://doi.org/10.48550/arXiv.2401.10603
    7. Lohrmann, C., Holm, C., & Datta, S. S. (2024). Influence of bacterial swimming and hydrodynamics on infection by phages. BioRxiv. https://doi.org/10.1101/2024.01.15.575727
    8. Zills, F., Schäfer, M. R., Tovey, S., Kästner, J., & Holm, C. (2024). Machine Learning-Driven Investigation of the Structure and Dynamics of the BMIM-BF₄ Room Temperature Ionic Liquid. Faraday Discuss. https://doi.org/10.1039/D4FD00025K
    9. Uhlig, F., Tovey, S., & Holm, C. (2024). Emergence of Accurate Atomic Energies from Machine Learned Noble Gas Potentials.
    10. Tovey, S., Lohrmann, C., & Holm, C. (2024). Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning. https://doi.org/10.48550/arXiv.2404.01999

  2. 2023

    1. Tischler, I., Schlaich, A., & Holm, C. (2023). Disentanglement of Surface and Confinement Effects for Diene Metathesis in Mesoporous Confinement. ACS Omega, 9(1), Article 1. https://doi.org/10.1021/acsomega.3c06195
    2. Beyer, D., Koss\fiovan, P., & Holm, C. (2023). Explaining Giant Apparent $pK_a$ Shifts in Weak Polyelectrolyte Brushes. Phys. Rev. Lett., 131(16), Article 16. https://doi.org/10.1103/PhysRevLett.131.168101
    3. Yang, J., Kondrat, S., Lian, C., Liu, H., Schlaich, A., & Holm, C. (2023). Solvent Effects on Structure and Screening in Confined Electrolytes. Phys. Rev. Lett., 131(11), Article 11. https://doi.org/10.1103/PhysRevLett.131.118201
    4. Schlaich, A., Tyagi, S., Kesselheim, S., Sega, M., & Holm, C. (2023). 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, 46(9), Article 9. https://doi.org/10.1140/epje/s10189-023-00334-2
    5. Tovey, S., Krippendorf, S., Nikolaou, K., & Holm, C. (2023). Towards a phenomenological understanding of neural networks: data. Machine Learning: Science and Technology, 4(3), Article 3. https://doi.org/10.1088/2632-2153/acf099
    6. Lohrmann, C., & Holm, C. (2023). A novel model for biofilm initiation in porous media flow. Soft Matter, 19(36), Article 36. https://doi.org/10.1039/D3SM00575E
    7. Jäger, H., Schlaich, A., Yang, J., Lian, C., Kondrat, S., & Holm, C. (2023). A screening of results on the decay length in concentrated electrolytes. Faraday Discuss., 246(0), Article 0. https://doi.org/10.1039/D3FD00043E
    8. Beyer, D., & Holm, C. (2023). A generalized grand-reaction method for modeling the exchange of weak (polyprotic) acids between a solution and a weak polyelectrolyte phase. The Journal of Chemical Physics, 159(1), Article 1. https://doi.org/10.1063/5.0155973
    9. Gravelle, S., Beyer, D., Brito, M., Schlaich, A., & Holm, C. (2023). Assessing the Validity of NMR Relaxation Rates Obtained from Coarse-Grained Simulations of PEG–Water Mixtures. The Journal of Physical Chemistry B, 127(25), Article 25. https://doi.org/10.1021/acs.jpcb.3c01646
    10. Gravelle, S., Beyer, D., Brito, M., Schlaich, A., & Holm, C. (2023). Assessing the validity of NMR relaxation rates obtained from coarse-grained simulations of PEG-water mixtures. https://doi.org/10.26434/chemrxiv-2022-f90tv-v4
    11. Gravelle, S., Haber-Pohlmeier, S., Mattea, C., Stapf, S., Holm, C., & Schlaich, A. (2023). NMR Investigation of Water in Salt Crusts: Insights from Experiments and Molecular Simulations. Langmuir, 39(22), Article 22. https://doi.org/10.1021/acs.langmuir.3c00036
    12. Tovey, S., Zills, F., Torres-Herrador, F., Lohrmann, C., Brückner, M., & Holm, C. (2023). MDSuite: comprehensive post-processing tool for particle simulations. Journal of Cheminformatics, 15(1), Article 1. https://doi.org/10.1186/s13321-023-00687-y
    13. Berberich, J., Fink, D., & Holm, C. (2023). Robustness of quantum algorithms against coherent control errors. https://doi.org/10.48550/arXiv.2303.00618
    14. Tovey, S., Zimmer, D., Lohrmann, C., Merkt, T., Koppenhoefer, S., Heuthe, V.-L., Bechinger, C., & Holm, C. (2023). Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning. https://doi.org/10.48550/arXiv.2307.00994
    15. Brito, M. E., Mikhtaniuk, S. E., Neelov, I. M., Borisov, O. V., & Holm, C. (2023). Implicit-Solvent Coarse-Grained Simulations of Linear–Dendritic Block Copolymer Micelles. International Journal of Molecular Sciences, 24(3), Article 3. https://doi.org/10.3390/ijms24032763
    16. Shavykin, O. V., Mikhtaniuk, S. E., Fatullaev, E. I., Neelov, I. M., Leermakers, F. A. M., Brito, M. E., Holm, C., Borisov, O. V., & Darinskii, A. A. (2023). Hybrid Molecules Consisting of Lysine Dendrons with Several Hydrophobic Tails: A SCF Study of Self-Assembling. International Journal of Molecular Sciences, 24(3), Article 3. https://doi.org/10.3390/ijms24032078
    17. Weeber, R., Kreissl, P., & Holm, C. (2023). Magnetic field controlled behavior of magnetic gels studied using particle-based simulations. Physical Sciences Reviews, 8(8), Article 8. https://doi.org/doi:10.1515/psr-2019-0106
    18. Weeber, R., Grad, J.-N., Beyer, D., Blanco, P. M., Kreissl, P., Reinauer, A., Tischler, I., Košovan, P., & Holm, C. (2023). ESPResSo, a Versatile Open-Source Software Package for Simulating Soft Matter Systems. In Reference Module in Chemistry, Molecular Sciences and Chemical Engineering. Elsevier. https://doi.org/10.1016/B978-0-12-821978-2.00103-3
    19. Finkbeiner, J., Tovey, S., & Holm, C. (2023). Generating Minimal Training Sets for Machine Learned Potentials. https://doi.org/10.48550/arXiv.2309.03840
    20. Kreissl, P., Holm, C., & Weeber, R. (2023). Interplay between steric and hydrodynamic interactions for ellipsoidal magnetic nanoparticles in a polymer suspension. Soft Matter, 19(6), Article 6. https://doi.org/10.1039/D2SM01428A
    21. Košovan, P., Landsgesell, J., Nová, L., Uhlík, F., Beyer, D., Blanco, P. M., Staňo, R., & Holm, C. (2023). Reply to the ‘Comment on “Simulations of ionization equilibria in weak polyelectrolyte solutions and gels”’ by J. Landsgesell, L. Nová, O. Rud, F. Uhlík, D. Sean, P. Hebbeker, C. Holm and P. Košovan, Soft Matter, 2019, 15, 1155–1185. Soft Matter, 19(19), Article 19. https://doi.org/10.1039/D3SM00155E
    22. Lohrmann, C., & Holm, C. (2023). Optimal motility strategies for self-propelled agents to explore porous media. https://doi.org/10.48550/arXiv.2302.06709
    23. Qiao, L., Szuttor, K., Holm, C., & Slater, G. W. (2023). Ratcheting Charged Polymers through Symmetric Nanopores Using Pulsed Fields: Designing a Low Pass Filter for Concentrating Polyelectrolytes. Nano Letters, 23(4), Article 4. https://pubs.acs.org/doi/10.1021/acs.nanolett.2c04588
    24. Berberich, J., Fink, D., Pranjić, D., Tutschku, C., & Holm, C. (2023). Training robust and generalizable quantum models. https://doi.org/10.48550/arXiv.2311.11871

  3. 2022

    1. Beyer, D., Kosovan, P., & Holm, C. (2022). Simulations Explain the Swelling Behavior of Hydrogels with Alternating Neutral and Weakly Acidic Blocks. Macromolecules, 55(23), Article 23. https://doi.org/10.1021/acs.macromol.2c01916
    2. Tischler, I., Weik, F., Kaufmann, R., Kuron, M., Weeber, R., & Holm, C. (2022). A thermalized electrokinetics model including stochastic reactions suitable for multiscale simulations of reaction–advection–diffusion systems. Journal of Computational Science, 63, 101770. https://doi.org/10.1016/j.jocs.2022.101770
    3. Landsgesell, J., Beyer, D., Hebbeker, P., Kosovan, P., & Holm, C. (2022). The pH-Dependent Swelling of Weak Polyelectrolyte Hydrogels Modeled at Different Levels of Resolution. Macromolecules, 55(8), Article 8. https://doi.org/10.1021/acs.macromol.1c02489
    4. Wang, W., Gardi, G., Malgaretti, P., Kishore, V., Koens, L., Son, D., Gilbert, H., Wu, Z., Harwani, P., Lauga, E., Holm, C., & Sitti, M. (2022). Order and information in the patterns of spinning magnetic micro-disks at the air-water interface. Science Advances, 8(2), Article 2. https://doi.org/10.1126/sciadv.abk0685
    5. Beyer, D., Landsgesell, J., Hebbeker, P., Rud, O., Lunkad, R., Kosovan, P., & Holm, C. (2022). Correction to “Grand-Reaction Method for Simulations of Ionization Equilibria Coupled to Ion Partitioning.” Macromolecules, 55(3), Article 3. https://doi.org/10.1021/acs.macromol.1c02672
    6. Tischler, I., Weik, F., Kaufmann, R., Kuron, M., Weeber, R., & Holm, C. (2022). A thermalized electrokinetics model including stochastic reactions suitable for multiscale simulations of reaction-advection-diffusion systems. Journal of Computational Science, 63, 101770. https://doi.org/10.1016/j.jocs.2022.101770

  4. 2021

    1. Zeman, J., Kondrat, S., & Holm, C. (2021). Ionic screening in bulk and under confinement. The Journal of Chemical Physics, 155(20), Article 20. https://doi.org/10.1063/5.0069340
    2. Szuttor, K., Kreissl, P., & Holm, C. (2021). A numerical investigation of analyte size effects in nanopore sensing systems. The Journal of Chemical Physics, 155(13), Article 13. https://doi.org/10.1063/5.0065085
    3. Riede, J. M., Holm, C., Schmitt, S., & Haeufle, D. F. B. (2021). The control effort to steer self-propelled microswimmers depends on their morphology: comparing symmetric spherical versus asymmetric              L              -shaped particles. Royal Society Open Science, 8(9), Article 9. https://doi.org/10.1098/rsos.201839
    4. Feuerstein, L., Biermann, C. G., Xiao, Z., Holm, C., & Simmchen, J. (2021). Highly Efficient Active Colloids Driven by Galvanic Exchange Reactions. Journal of the American Chemical Society, 143(41), Article 41. https://doi.org/10.1021/jacs.1c06400
    5. Riede, J. M., Holm, C., Schmitt, S., & Haeufle, D. F. B. (2021). The control effort to steer self-propelled microswimmers depends on their morphology: comparing symmetric spherical versus asymmetric              $łess$i$\greater$L$łess$/i$\greater$              -shaped particles. Royal Society Open Science, 8(9), Article 9. https://doi.org/10.1098/rsos.201839
    6. Rud, O. V., Landsgesell, J., Holm, C., & Kosovan, P. (2021). Modeling of weak polyelectrolyte hydrogels under compression – Implications for water desalination. Desalination, 506, 114995. https://doi.org/10.1016/j.desal.2021.114995
    7. Stano, R., Kosovan, P., Tagliabue, A., & Holm, C. (2021). Electrostatically Cross-Linked Reversible Gels—Effects of pH and Ionic Strength. Macromolecules, 54(10), Article 10. https://doi.org/10.1021/acs.macromol.1c00470
    8. Wagner, A., Eggenweiler, E., Weinhardt, F., Trivedi, Z., Krach, D., Lohrmann, C., Jain, K., Karadimitriou, N., Bringedal, C., Voland, P., Holm, C., Class, H., Steeb, H., & Rybak, I. (2021). Permeability Estimation of Regular Porous Structures: A Benchmark for Comparison of Methods. Transport in Porous Media, 138(1), Article 1. https://doi.org/10.1007/s11242-021-01586-2
    9. Szuttor, K., Weik, F., Grad, J.-N., & Holm, C. (2021). Modeling the current modulation of bundled DNA structures in nanopores. The Journal of Chemical Physics, 154(5), Article 5. https://doi.org/10.1063/5.0038530
    10. Finkbeiner, J., Tovey, S., & Holm, C. (2021). Efficient Data Selection Methods for the Development of Machine Learned Potentials. ArXiv, abs/2108.01582.
    11. Tagliabue, A., Landsgesell, J., Mella, M., & Holm, C. (2021). Can oppositely charged polyelectrolyte stars form a gel? A simulational study. Soft Matter. https://doi.org/10.1039/D0SM01617A
    12. Lee, M., Lohrmann, C., Szuttor, K., Auradou, H., & Holm, C. (2021). The influence of motility on bacterial accumulation in a microporous channel. Soft Matter, 17(4), Article 4. https://doi.org/10.1039/D0SM01595D
    13. Kreissl, P., Holm, C., & Weeber, R. (2021). Frequency-dependent magnetic susceptibility of magnetic nanoparticles in a polymer solution: a simulation study. Soft Matter, 17(1), Article 1. https://doi.org/10.1039/D0SM01554G
    14. Kuron, M., Stewart, C., de Graaf, J., & Holm, C. (2021). An extensible lattice Boltzmann method for viscoelastic flows: complex and moving boundaries in Oldroyd-B fluids. https://doi.org/10.1140/epje/s10189-020-00005-6
    15. Itto, Y. (2021). Fluctuating Diffusivity of RNA-Protein Particles: Analogy with Thermodynamics. Entropy, 23(3), Article 3. https://doi.org/10.3390/e23030333

  5. 2020

    1. Tischler, I., Schlaich, A., & Holm, C. (2020). The Presence of a Wall Enhances the Probability for Ring-Closing Metathesis: Insights from Classical Polymer Theory and Atomistic Simulations. Macromolecular Theory and Simulations, 2000076. https://doi.org/10.1002/mats.202000076
    2. Breitsprecher, K., Janssen, M., Srimuk, P., Mehdi, B. L., Presser, V., Holm, C., & Kondrat, S. (2020). How to speed up ion transport in nanopores. Nature Communications, 11(1), Article 1. https://doi.org/10.1038/s41467-020-19903-6
    3. Tovey, S., Krishnamoorthy, A. N., Sivaraman, G., Guo, J., Benmore, C., Heuer, A., & Holm, C. (2020). DFT Accurate Interatomic Potential for Molten NaCl from Machine Learning. The Journal of Physical Chemistry C, 124(47), Article 47. https://doi.org/10.1021/acs.jpcc.0c08870
    4. Sivaraman, G., Krishnamoorthy, A. N., Baur, M., Holm, C., Stan, M., Csányi, G., Benmore, C., & Vázquez-Mayagoitia, Á. (2020). Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide. Npj Computational Materials, 6(1), Article 1. https://doi.org/10.1038/s41524-020-00367-7
    5. Landsgesell, J., Hebbeker, P., Rud, O., Lunkad, R., Kosovan, P., & Holm, C. (2020). Grand-Reaction Method for Simulations of Ionization Equilibria Coupled to Ion Partitioning. Macromolecules, 53(8), Article 8. https://doi.org/10.1021/acs.macromol.0c00260
    6. Sánchez, P. A., Vögele, M., Smiatek, J., Qiao, B., Sega, M., & Holm, C. (2020). PDADMAC/PSS Oligoelectrolyte Multilayers: Internal Structure and Hydration Properties at Early Growth Stages from Atomistic Simulations. Molecules, 25(8), Article 8. https://doi.org/10.3390/molecules25081848
    7. Landsgesell, J., Sean, D., Kreissl, P., Szuttor, K., & Holm, C. (2020). Erratum: Modeling Gel Swelling Equilibrium in the Mean Field: From Explicit to Poisson-Boltzmann Models Phys. Rev. Lett. 122, 208002 (2019). Phys. Rev. Lett., 124(11), Article 11. https://doi.org/10.1103/PhysRevLett.124.119901
    8. Zeman, J., Kondrat, S., & Holm, C. (2020). Bulk ionic screening lengths from extremely large-scale molecular dynamics simulations. Chem. Commun., 56(100), Article 100. https://doi.org/10.1039/D0CC05023G
    9. Flemisch, B., Hermann, S., Holm, C., Mehl, M., Reina, G., Uekermann, B., Boehringer, D., Ertl, T., Grad, J.-N., Iglezakis, D., Jaust, A., Koch, T., Seeland, A., Weeber, R., Weik, F., & Weishaupt, K. (2020). Umgang mit Forschungssoftware an der Universität Stuttgart. Universität Stuttgart. https://doi.org/10.18419/OPUS-11178

  6. 2019

    1. Landsgesell, J., & Holm, C. (2019). Cell Model Approaches for Predicting the Swelling and Mechanical Properties of Polyelectrolyte Gels. Macromolecules, 52(23), Article 23. https://doi.org/10.1021/acs.macromol.9b01216
    2. Zeman, J., Holm, C., & Smiatek, J. (2019). The Effect of Small Organic Cosolutes on Water Structure and Dynamics. Journal of Chemical & Engineering Data, 65(3), Article 3. https://doi.org/10.1021/acs.jced.9b00577
    3. Weeber, R., Nestler, F., Weik, F., Pippig, M., Potts, D., & Holm, C. (2019). Accelerating the calculation of dipolar interactions in particle based simulations with open boundary conditions by means of the P2NFFT method. Journal of Computational Physics, 391, 243--258. https://doi.org/10.1016/j.jcp.2019.01.044
    4. Lee, M., Szuttor, K., & Holm, C. (2019). A computational model for bacterial run-and-tumble motion. The Journal of Chemical Physics, 150(17), Article 17. https://doi.org/10.1063/1.5085836
    5. Landsgesell, J., Sean, D., Kreissl, P., Szuttor, K., & Holm, C. (2019). Modeling Gel Swelling Equilibrium in the Mean Field: From Explicit to Poisson-Boltzmann Models. Phys. Rev. Lett., 122(20), Article 20. https://doi.org/10.1103/PhysRevLett.122.208002
    6. Kuron, M., Stärk, P., Burkard, C., de Graaf, J., & Holm, C. (2019). A lattice Boltzmann model for squirmers. The Journal of Chemical Physics, 150(14), Article 14. https://doi.org/10.1063/1.5085765
    7. Holm, C., Ertl, T., Schmauder, S., Kästner, J., & Gross, J. (2019). Particle methods in natural science and engineering. The European Physical Journal Special Topics, 227(14), Article 14. https://doi.org/10.1140/epjst/e2019-900008-2
    8. Roy, T., Szuttor, K., Smiatek, J., Holm, C., & Hardt, S. (2019). Conformation and Dynamics of Long-Chain End-Tethered Polymers in Microchannels. Polymers, 11(3), Article 3. https://doi.org/10.3390/polym11030488
    9. Weik, F., Weeber, R., Szuttor, K., Breitsprecher, K., de Graaf, J., Kuron, M., Landsgesell, J., Menke, H., Sean, D., & Holm, C. (2019). ESPResSo 4.0 -- an extensible software package for simulating soft matter systems. The European Physical Journal Special Topics, 227(14), Article 14. https://doi.org/10.1140/epjst/e2019-800186-9
    10. Weik, F., Szuttor, K., Landsgesell, J., & Holm, C. (2019). Modeling the current modulation of dsDNA in nanopores -- from mean-field to atomistic and back. The European Physical Journal Special Topics, 227(14), Article 14. https://doi.org/10.1140/epjst/e2019-800189-3
    11. Sean, D., Landsgesell, J., & Holm, C. (2019). Influence of weak groups on polyelectrolyte mobilities. ELECTROPHORESIS, 40(5), Article 5. https://doi.org/10.1002/elps.201800346
    12. Weeber, R., Kreissl, P., & Holm, C. (2019). Studying the field-controlled change of shape and elasticity of magnetic gels using particle-based simulations. Archive of Applied Mechanics, 89(1), Article 1. https://doi.org/10.1007/s00419-018-1396-4
    13. Smiljanic, M., Weeber, R., Pflüger, D., Holm, C., & Kronenburg, A. (2019). Developing coarse-grained models for agglomerate growth. The European Physical Journal Special Topics, 227(14), Article 14. https://doi.org/10.1140/epjst/e2018-800177-y
    14. Kuron, M., Stärk, P., Holm, C., & de Graaf, J. (2019). Hydrodynamic mobility reversal of squirmers near flat and curved surfaces. Soft Matter, 15(29), Article 29. https://doi.org/10.1039/C9SM00692C
    15. Sánchez, P. A., Vögele, M., Smiatek, J., Qiao, B., Sega, M., & Holm, C. (2019). Atomistic simulation of PDADMAC/PSS oligoelectrolyte multilayers: overall comparison of tri- and tetra-layer systems. Soft Matter, 15(46), Article 46. https://doi.org/10.1039/C9SM02010A
    16. Landsgesell, J., Nová, L., Rud, O., Uhlík, F., Sean, D., Hebbeker, P., Holm, C., & Košovan, P. (2019). Simulations of ionization equilibria in weak polyelectrolyte solutions and gels. Soft Matter, 15(6), Article 6. https://doi.org/10.1039/C8SM02085J
    17. Arens, L., Barther, D., Landsgesell, J., Holm, C., & Wilhelm, M. (2019). Poly(sodium acrylate) hydrogels: synthesis of various network architectures, local molecular dynamics, salt partitioning, desalination and simulation. Soft Matter, 15(48), Article 48. https://doi.org/10.1039/C9SM01468C

  7. 2018

    1. Kuron, M., Kreissl, P., & Holm, C. (2018). Toward Understanding of Self-Electrophoretic Propulsion under Realistic Conditions: From Bulk Reactions to Confinement Effects. Accounts of Chemical Research, 51(12), Article 12. https://doi.org/10.1021/acs.accounts.8b00285
    2. Michalowsky, J., Zeman, J., Holm, C., & Smiatek, J. (2018). A polarizable MARTINI model for monovalent ions in aqueous solution. The Journal of Chemical Physics, 149(16), Article 16. https://doi.org/10.1063/1.5028354
    3. Breitsprecher, K., Holm, C., & Kondrat, S. (2018). Charge Me Slowly, I Am in a Hurry: Optimizing Charge–Discharge Cycles in Nanoporous Supercapacitors. ACS Nano, 12(10), Article 10. https://doi.org/10.1021/acsnano.8b04785
    4. Weyman, A., Bier, M., Holm, C., & Smiatek, J. (2018). Microphase separation and the formation of ion conductivity channels in poly(ionic liquid)s: A coarse-grained molecular dynamics study. The Journal of Chemical Physics, 148(19), Article 19. https://doi.org/10.1063/1.5016814
    5. Krishnamoorthy, A. N., Holm, C., & Smiatek, J. (2018). Influence of Cosolutes on Chemical Equilibrium: a Kirkwood–Buff Theory for Ion Pair Association–Dissociation Processes in Ternary Electrolyte Solutions. The Journal of Physical Chemistry C, 122(19), Article 19. https://doi.org/10.1021/acs.jpcc.7b12255
    6. Weeber, R., Hermes, M., Schmidt, A. M., & Holm, C. (2018). Polymer architecture of magnetic gels: a review. Journal of Physics: Condensed Matter, 30(6), Article 6. https://doi.org/10.1088/1361-648x/aaa344
    7. Uhlig, F., Zeman, J., Smiatek, J., & Holm, C. (2018). First-Principles Parametrization of Polarizable Coarse-Grained Force Fields for Ionic Liquids. Journal of Chemical Theory and Computation, 14(3), Article 3. https://doi.org/10.1021/acs.jctc.7b00903
    8. Hartmann, J., Roy, T., Szuttor, K., Smiatek, J., Holm, C., & Hardt, S. (2018). Relaxation of surface-tethered polymers under moderate confinement. Soft Matter, 14(38), Article 38. https://doi.org/10.1039/C8SM01246F
    9. Sean, D., Landsgesell, J., & Holm, C. (2018). Computer Simulations of Static and Dynamical Properties of Weak Polyelectrolyte Nanogels in Salty Solutions. Gels, 4(1), Article 1. https://doi.org/10.3390/gels4010002
    10. Smiatek, J., & Holm, C. (2018). From the Atomistic to the Macromolecular Scale: Distinct Simulation Approaches for Polyelectrolyte Solutions. In W. Andreoni & S. Yip (Eds.), Handbook of Materials Modeling : Methods: Theory and Modeling (pp. 1--15). Springer International Publishing. https://doi.org/10.1007/978-3-319-42913-7_33-1
    11. Narayanan Krishnamoorthy, A., Holm, C., & Smiatek, J. (2018). Specific ion effects for polyelectrolytes in aqueous and non-aqueous media: the importance of the ion solvation behavior. Soft Matter, 14(30), Article 30. https://doi.org/10.1039/C8SM00600H
    12. Narayanan Kirshnamoorthy, A., Oldiges, K., Winter, M., Heuer, A., Cekic-Laskovic, I., Holm, C., & Smiatek, J. (2018). Electrolyte solvents for high voltage lithium ion batteries: ion correlation and specific anion effects in adiponitrile. Phys. Chem. Chem. Phys., 20(40), Article 40. https://doi.org/10.1039/C8CP04102D

  8. 2017

    1. Breitsprecher, K., Abele, M., Kondrat, S., & Holm, C. (2017). The effect of finite pore length on ion structure and charging. The Journal of Chemical Physics, 147(10), Article 10. https://doi.org/10.1063/1.4986346
    2. Roy, T., Szuttor, K., Smiatek, J., Holm, C., & Hardt, S. (2017). Electric-field-induced stretching of surface-tethered polyelectrolytes in a microchannel. Phys. Rev. E, 96(3), Article 3. https://doi.org/10.1103/PhysRevE.96.032503
    3. Richter, T., Landsgesell, J., Kosovan, P., & Holm, C. (2017). On the efficiency of a hydrogel-based desalination cycle. Desalination, 414, 28--34. https://doi.org/10.1016/j.desal.2017.03.027
    4. Szuttor, K., Roy, T., Hardt, S., Holm, C., & Smiatek, J. (2017). The stretching force on a tethered polymer in pressure-driven flow. The Journal of Chemical Physics, 147(3), Article 3. https://doi.org/10.1063/1.4993619
    5. Chung, S., Samin, S., Holm, C., Malherbe, J. G., & Amokrane, S. (2017). Dynamics of field-driven population inversion in a confined colloidal mixture. Phys. Rev. E, 95(2), Article 2. https://doi.org/10.1103/PhysRevE.95.022605
    6. Michalowsky, J., Schäfer, L. V., Holm, C., & Smiatek, J. (2017). A refined polarizable water model for the coarse-grained MARTINI force field with long-range electrostatic interactions. The Journal of Chemical Physics, 146(5), Article 5. https://doi.org/10.1063/1.4974833
    7. Landsgesell, J., Holm, C., & Smiatek, J. (2017). Wang–Landau Reaction Ensemble Method: Simulation of Weak Polyelectrolytes and General Acid–Base Reactions. Journal of Chemical Theory and Computation, 13(2), Article 2. https://doi.org/10.1021/acs.jctc.6b00791

ResearcherID: C-2143-2013

ORCID: 0000-0003-2739-310X

Google Scholar: Christian Holm

Some of our research is part of collaborative research centres such as: 

SFB 716 sfb716.uni-stuttgart.de

SFB 1313 sfb1313.uni-stuttgart.de

SFB 1333 crc1333.uni-stuttgart.de

FOR 2811 FOR2811.uni-mainz.de

SimTech SimTech Cluster of Excellence

NanodiagBW Clusters4Future NanodiaBW

MultiXscale EuroHPC JU Centre of Excellence

CECAM CECAM Node Soft Matter and Statistical Mechanics (SMSM)

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