This image showsChristian 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.

You can find my video portrait from 2015 here.

  1. 2026

    1. Beyer, D., & Holm, C. (2026). Charge Regulation and Swelling of Weak Polyelectrolyte Nanogels in Divalent Salt Solutions. Macromolecular Chemistry and Physics, 227, Article 5. https://doi.org/10.1002/macp.202500476
    2. Kurahatti, S., Brito, M. E., Beyer, D., & Holm, C. (2026). Effect of Different Network Topologies on Swelling and Mechanical Properties of Polyelectrolyte Hydrogels. Macromolecules. https://doi.org/10.1021/acs.macromol.5c03180
    3. Beyer, D., Holm, C., & Wang, Z.-G. (2026). A Sequence-Specific Theory for Charge-Regulating IDPs. https://doi.org/10.26434/chemrxiv.10001808/v1
    4. Radhakrishnan, K., Beyer, D., & Holm, C. (2026). Charge Regulation and Orientation Dictate Protein Uptake into Polyelectrolyte Brushes. https://doi.org/10.26434/chemrxiv.10001926/v1
    5. Grün, J. J., Beyer, D., Mons, P. J., Seitel, S., Fribiczer, N., Poudel, P., Könemann, N., Zank, L. K. R. J., Zylla, P. F., Košovan, P., Seiffert, S., Holm, C., & Schacher, F. H. (2026). Triggered Dissolution of Electrostatically Crosslinked Hydrogels from Star‐Shaped Polyampholytic Block Copolymers. Macromolecular Chemistry and Physics, 227, Article 4. https://doi.org/10.1002/macp.202500499
    6. Reinauer, A., Kondrat, S., & Holm, C. (2026). Asymmetric Ionic Liquids for Enhanced Performance of Nanoporous Electrical Double Layer Capacitors. The Journal of Physical Chemistry C, 130, Article 10. https://doi.org/10.1021/acs.jpcc.5c08058
    7. Nikolaou, K., Krippendorf, S., Tovey, S., & Holm, C. (2026). Beyond scaling curves: internal dynamics of neural networks through the NTK lens. Machine Learning: Science and Technology, 7, Article 2. https://doi.org/10.1088/2632-2153/ae4442
    8. Fellner, T., Kreplin, D., Tovey, S., & Holm, C. (2026). Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning. Machine Learning: Science and Technology. https://doi.org/10.1088/2632-2153/ae365f
    9. Fellner, T., Merklinger, J., & Holm, C. (2026). Multivariate quantum reservoir computing with discrete and continuous variable systems. https://arxiv.org/abs/2604.08427

  2. 2025

    1. Agrawal, A., Gravelle, S., Holm, C., & Schlaich, A. (2025). Interfacial ion hydration and electrostatics govern salt precipitation and crystal morphology. The Journal of Chemical Physics, 163, Article 24. https://doi.org/10.1063/5.0299741
    2. Grad, J.-N., Chourdakis, G., Flemisch, B., Holm, C., Keim, L., Uekermann, B., & Weeber, R. (2025). Organizing software community workshops: Experiences from three independent simulation software projects. Electronic Communications of the EASST, 85. https://doi.org/10.14279/eceasst.v85.2700
    3. Tovey, S., Holm, C., & Smiatek, J. (2025). Self- and Fick Diffusion Coefficients in Implicit Solvent Simulations: Influence of Local Aggregation Effects and Thermodynamic Factors. Liquids, 5, Article 4. https://doi.org/10.3390/liquids5040036
    4. Kurahatti, S., Brito, M. E., Beyer, D., & Holm, C. (2025). Effect of Different Network Topologies on Swelling and Mechanical Properties of Polyelectrolyte Hydrogels. https://doi.org/10.26434/chemrxiv-2025-r7pd7
    5. Reinauer, A., Oberer, L., Schlaich, A., & Holm, C. (2025). A lattice Boltzmann approach for modeling coupled evaporation/precipitation processes in porous media. https://doi.org/10.22541/essoar.176245741.14271076/v1
    6. Batatia, I., Benner, P., Chiang, Y., Elena, A. M., Kovács, D. P., Riebesell, J., Advincula, X. R., Asta, M., Avaylon, M., Baldwin, W. J., Berger, F., Bernstein, N., Bhowmik, A., Bigi, F., Blau, S. M., Cărare, V., Ceriotti, M., Chong, S., Darby, J. P., et al. (2025). A foundation model for atomistic materials chemistry. The Journal of Chemical Physics, 163, Article 18. https://doi.org/10.1063/5.0297006
    7. Radhakrishnan, K., & Holm, C. (2025). How Patch-Induced Charge Regulation Drives Adsorption of Proteins into Polyelectrolyte Brushes. https://doi.org/10.26434/chemrxiv-2025-8mrgm
    8. Gravelle, S., Coasne, B., Holm, C., & Schlaich, A. (2025). Intermittent molecular motion and first passage statistics for the NMR relaxation of confined water. Physical Review E, 112, Article 3. https://doi.org/10.1103/1j21-bqdm
    9. Beyer, D., Holm, C., & Wang, Z.-G. (2025). Charge Regulation Effects in Weak Polyelectrolyte Complexation. The Journal of Physical Chemistry Letters, 8245–8251. https://doi.org/10.1021/acs.jpclett.5c01877
    10. Tovey, S., Krippendorf, S., Spannowsky, M., Nikolaou, K., & Holm, C. (2025). Collective variables of neural networks: empirical time evolution and scaling laws. Machine Learning: Science and Technology, 6, Article 3. https://doi.org/10.1088/2632-2153/adee76
    11. Elijosius, R., Zills, F., Batatia, I., Norwood, S. W., Kovács, D. P., Holm, C., & Csányi, G. (2025). Zero shot molecular generation via similarity kernels. Nature Communications, 16, Article 1. https://doi.org/10.1038/s41467-025-60963-3
    12. Zills, F., Agarwal, S., Goncalves, T., Gupta, S., Fako, E., Han, S., Mueller, I., Holm, C., & De, S. (2025). MLIPX: Machine Learned Interatomic Potential eXploration. https://doi.org/10.26434/chemrxiv-2025-0v720
    13. Bozkurt, K., Lohrmann, C., Weinhardt, F., Hanke, D., Hopp, R., Gerlach, R., Holm, C., & Class, H. (2025). Intermittent flow paths in biofilms grown in a microfluidic channel. Advances in Water Resources, 105018. https://doi.org/10.1016/j.advwatres.2025.105018
    14. Burth, L., Beyer, D., & Holm, C. (2025). A Comparison of Bead‐Spring and Site‐Binding Models for Weak Polyelectrolytes. Macromolecular Theory and Simulations, 34, Article 4. https://doi.org/10.1002/mats.202500020
    15. Beyer, D., Holm, C., & Wang, Z.-G. (2025). Charge Regulation Dramatically Enhances Weak Polyelectrolyte Complexation. https://doi.org/10.26434/chemrxiv-2025-tkcg2
    16. Uhlig, F., Tovey, S., & Holm, C. (2025). Emergence of accurate atomic energies from machine-learned noble-gas potentials. The Journal of Chemical Physics, 162, Article 18. https://doi.org/10.1063/5.0227640
    17. Tovey, S., Lohrmann, C., Merkt, T., Zimmer, D., Nikolaou, K., Koppenhöfer, S., Bushmakina, A., Scheunemann, J., & Holm, C. (2025). SwarmRL: building the future of smart active systems. The European Physical Journal E, 48, Article 4. https://doi.org/10.1140/epje/s10189-025-00477-4
    18. Tovey, S., Fellner, T., Holm, C., & Spannowsky, M. (2025). Generating quantum reservoir state representations with random matrices. Machine Learning: Science and Technology, 6, Article 1. https://doi.org/10.1088/2632-2153/adc0e2
    19. Brito, M. E., Höpner, E., Beyer, D., & Holm, C. (2025). Modeling Swelling of pH-Responsive Microgels: Theory and Simulations. Macromolecules. https://doi.org/10.1021/acs.macromol.4c03124
    20. Berberich, J., Fellner, T., & Holm, C. (2025). The interplay of robustness and generalization in quantum machine learning. https://arxiv.org/abs/2506.08455
    21. Berberich, J., Fellner, T., Kosut, R. L., & Holm, C. (2025). Robustness of quantum algorithms: Worst-case fidelity bounds and implications for design. https://arxiv.org/abs/2509.08481
    22. Nikolaou, K., Krippendorf, S., Tovey, S., & Holm, C. (2025). Beyond Scaling Curves: Internal Dynamics of Neural Networks Through the NTK Lens. https://arxiv.org/abs/2507.05035
    23. Tovey, S., Hoßbach, J., Kuppel, S., Ensslen, T., Behrends, J. C., & Holm, C. (2025). Deep Learning-Driven Peptide Classification in Biological Nanopores. https://arxiv.org/abs/2509.14029
    24. Fellner, T., Kreplin, D., Tovey, S., & Holm, C. (2025). Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning. https://arxiv.org/abs/2504.12416
    25. Reinauer, A., Oberer, L., Schlaich, A., & Holm, C. (2025). Replication Data and Scripts for: A lattice Boltzmann approach for modeling coupled evaporation/precipitation processes in porous media. DaRUS. https://doi.org/10.18419/DARUS-5389

  3. 2024

    1. Berberich, J., Fink, D., Pranjić, D., Tutschku, C., & Holm, C. (2024). Training robust and generalizable quantum models. Physical Review Research, 6, Article 4. https://doi.org/10.1103/physrevresearch.6.043326
    2. Reinauer, A., Kondrat, S., & Holm, C. (2024). Electrolytes in conducting nanopores: Revisiting constant charge and constant potential simulations. The Journal of Chemical Physics, 161, Article 10. https://doi.org/10.1063/5.0226959
    3. Radhakrishnan, K., Beyer, D., & Holm, C. (2024). How Charge Regulation Affect Protein Uptake in Weak Polyelectrolyte Brushes. https://doi.org/10.26434/chemrxiv-2024-b10lj
    4. Beyer, D., Blanco, P. M., Landsgesell, J., Kosovan, P., & Holm, C. (2024). How to Correct Systematic Errors in Constant-pH Ensemble Simulations. https://doi.org/10.26434/chemrxiv-2024-d4dbh
    5. Tovey, S., Lohrmann, C., & Holm, C. (2024). Emergence of chemotactic strategies with multi-agent reinforcement learning. Machine Learning: Science and Technology, 5, Article 3. https://doi.org/10.1088/2632-2153/ad5f73
    6. Hoßbach, J., Tovey, S., Ensslen, T., Behrends, J. C., & Holm, C. (2024). Peptide Classification from Statistical Analysis of Nanopore Translocation Experiments. https://doi.org/doi.org/10.48550/arXiv.2408.14275
    7. Beyer, D., & Holm, C. (2024). Unexpected Two-Stage Swelling of Weak Polyelectrolyte Brushes with Divalent Counterions. https://doi.org/10.26434/chemrxiv-2024-xxjr1
    8. Tovey, S., Lohrmann, C., Merkt, T., Zimmer, D., Nikolaou, K., Koppenhöfer, S., Bushmakina, A., Scheunemann, J., & Holm, C. (2024). SwarmRL: Building the Future of Smart Active Systems. https://doi.org/10.48550/arXiv.2404.16388
    9. 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
    10. Tovey, S., Holm, C., & Spannowsky, M. (2024). Generating Reservoir State Descriptions with Random Matrices. https://doi.org/arXiv:2404.07278
    11. Brito, M. E., & Holm, C. (2024). Modelling microgel swelling: Influence of chain finite extensibility. https://doi.org/10.26434/chemrxiv-2024-h8q4c
    12. 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
    13. Berberich, J., Fink, D., & Holm, C. (2024). Robustness of quantum algorithms against coherent control errors. Phys. Rev. A, 109, Article 1. https://doi.org/10.1103/PhysRevA.109.012417
    14. Zills, F., Schäfer, M., Tovey, S., Kästner, J., & Holm, C. (2024). ZnTrack -- Data as Code. https://doi.org/10.48550/arXiv.2401.10603
    15. 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
    16. 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
    17. Weeber, R., Grad, J.-N., Beyer, D., Blanco, P. M., Kreissl, P., Reinauer, A., Tischler, I., Košovan, P., & Holm, C. (2024). ESPResSo, a Versatile Open-Source Software Package for Simulating Soft Matter Systems. In M. Yáñez & R. J. Boyd (Eds.), Comprehensive Computational Chemistry (First Edition) (First Edition, pp. 578–601). Elsevier. https://doi.org/10.1016/B978-0-12-821978-2.00103-3
    18. Vogel, P., Beyer, D., Holm, C., & Palberg, T. (2024). CO2-induced Drastic Decharging of Dielectric Surfaces in Aqueous Suspensions. https://doi.org/doi.org/10.48550/arXiv.2409.03049
    19. Vogel, P., Bayer, D., Holm, C., & Palberg, T. (2024). CO2-induced Drastic Decharging of Dielectric Surfaces in Aqueous Suspensions. https://arxiv.org/abs/2401.00096
    20. Uhlig, F., Tovey, S., & Holm, C. (2024). Emergence of Accurate Atomic Energies from Machine Learned Noble Gas Potentials.

  4. 2023

    1. Tischler, I., Schlaich, A., & Holm, C. (2023). Disentanglement of Surface and Confinement Effects for Diene Metathesis in Mesoporous Confinement. ACS Omega, 9, 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, Article 16. https://doi.org/10.1103/PhysRevLett.131.168101
    3. Beyer, D., Kosiovan, P., & Holm, C. (2023). Explaining Giant Apparent pKa Shifts in Weak Polyelectrolyte Brushes. Phys. Rev. Lett., 131, Article 16. https://doi.org/10.1103/PhysRevLett.131.168101
    4. 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, Article 11. https://doi.org/10.1103/PhysRevLett.131.118201
    5. 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, Article 9. https://doi.org/10.1140/epje/s10189-023-00334-2
    6. Tovey, S., Krippendorf, S., Nikolaou, K., & Holm, C. (2023). Towards a phenomenological understanding of neural networks: data. Machine Learning: Science and Technology, 4, Article 3. https://doi.org/10.1088/2632-2153/acf099
    7. Lohrmann, C., & Holm, C. (2023). A novel model for biofilm initiation in porous media flow. Soft Matter, 19, Article 36. https://doi.org/10.1039/D3SM00575E
    8. 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, Article 0. https://doi.org/10.1039/D3FD00043E
    9. 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, Article 1. https://doi.org/10.1063/5.0155973
    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., 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, Article 25. https://doi.org/10.1021/acs.jpcb.3c01646
    12. 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, Article 22. https://doi.org/10.1021/acs.langmuir.3c00036
    13. 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, Article 1. https://doi.org/10.1186/s13321-023-00687-y
    14. Berberich, J., Fink, D., & Holm, C. (2023). Robustness of quantum algorithms against coherent control errors. https://doi.org/10.48550/arXiv.2303.00618
    15. 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
    16. 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, Article 3. https://doi.org/10.3390/ijms24032763
    17. 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, Article 3. https://doi.org/10.3390/ijms24032078
    18. Weeber, R., Kreissl, P., & Holm, C. (2023). Magnetic field controlled behavior of magnetic gels studied using particle-based simulations. Physical Sciences Reviews, 8, Article 8. https://doi.org/doi:10.1515/psr-2019-0106
    19. 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
    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, Article 6. https://doi.org/10.1039/D2SM01428A
    21. Finkbeiner, J., Tovey, S., & Holm, C. (2023). Generating Minimal Training Sets for Machine Learned Potentials. https://doi.org/10.48550/arXiv.2309.03840
    22. 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, Article 19. https://doi.org/10.1039/D3SM00155E
    23. Lohrmann, C., & Holm, C. (2023). Optimal motility strategies for self-propelled agents to explore porous media. https://doi.org/10.48550/arXiv.2302.06709
    24. 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, Article 4. https://pubs.acs.org/doi/10.1021/acs.nanolett.2c04588

  5. 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, 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, 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, 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, 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

  6. 2021

    1. Zeman, J., Kondrat, S., & Holm, C. (2021). Ionic screening in bulk and under confinement. The Journal of Chemical Physics, 155, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, Article 3. https://doi.org/10.3390/e23030333

  7. 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

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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