Book of Abstracts of the 2025 ESPResSo Summer School

CECAM Flagship School Systematic coarse-graining and machine learning in soft matter physics with ESPResSo Book of Abstracts Stuttgart, Germany October 6–10, 2025 Book of Abstracts 2025 ESPResSo Summer School CECAM Flagship School “Systematic coarse-graining and machine learning in soft matter physics with ESPResSo” Stuttgart, Germany October 6–10, 2025 Organizing committee Tristan Bereau, Christian Holm, Alexander Schlaich, Jean-Noël Grad, Rudolf Weeber Edited by Jean-Noël Grad ii The 2025 CECAM Flagship School “Systematic coarse-graining and machine learning in soft matter physics with ESPResSo” was organized by the Institute for Computational Physics (ICP, www.icp.uni-stuttgart.de) in Stuttgart, Germany, and hosted by the Soft Matter and Statistical Mechanics CECAM node (CECAM-DE-SMSM, www.cecam.org/cecam-de-smsm). We acknowledge the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, www.dfg.de) via grant no. 528726435, the Institute for Computational Physics and the Cluster of Excellence SimTech for funding the event. The participation of Jean-Noël Grad in this event was partially or wholly funded by the European Union, and has received funding from the European High Performance Computing Joint Undertaking (EuroHPC JU) and countries participating in the project under grant agreement no. 101093169, and from the German Federal Ministry of Education and Research (BMBF) under grant no. 16HPC095. The abstracts compiled in this book comprise the proceedings of the CECAM Flagship School. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the sponsors. Neither the CECAM-DE-SMSM node, SimTech, the DFG, the BMBF, the European Union, nor the EuroHPC JU and countries participating in the project can be held responsible for them. DOI: 10.5281/zenodo.17362577. Publisher Institute for Computational Physics University of Stuttgart Allmandring 3 70569 Stuttgart Germany Copyright © 2025 by the Institute for Computational Physics (ICP) This work is licensed under a Creative Commons “Attribution 4.0 International” license. Copyright for components of this work owned by others than the ICP must be honored, such as logos and material explicitly reproduced with permission from other sources. iii Contents Talks 2 3Introduction to machine learning Konstantin Nikolaou 3Structural representations for atomistic machine learning Philip Loche 3Machine learning in multiscale modeling for molecular discovery and backmapping Tristan Bereau 4Coarse-graining: insights and applications Denis Andrienko 4Machine learning potentials: from efficient coarse-grained models to large-scale deployment Julija Zavadlav 4Reinforcement learning for intelligent active matter Samuel Tovey 4FAIRMD: quality-evaluated all-atom MD trajectories for your model-training pleasure Markus Miettinen 5Large time-step all-atom molecular dynamics with deep generative models Simon Olsson 5Introduction to the simulation of soft matter systems Alexander Schlaich 5Polymer dynamics and treatment of charged systems Christian Holm 5Inverse coarse-graining for parameterizing colored-noise thermostats in soft matter simulations Nico van der Vegt 6Introduction to ESPResSo Jean-Noël Grad 6Coupling molecular dynamics and lattice-Boltzmann hydrodynamics Rudolf Weeber 6Coarse-grained modelling of G-quadruplex multimers Deniz Mostarac, Cristiano De Michele Poster session 6 8MLIPX: a framework for evaluating machine-learned interatomic potential Fabian Zills, Sheena Agarwal, Edvin Fako, Shuang Han, Srishti Gupta, Tiago J. Goncalves, Imke Britta Mueller, Christian Holm, Sandip De 8Morphological analysis of agglomerate size effect on the thermal restructuring kinetics of DLCA fractal agglomerates Jahanbakhsh Jahanzamin, José Morán, M. Reza Kholghy 9Controlling charging dynamics of electrolytic capacitors Megh Dutta, Benjamin Rotenberg, Emmanuel Trizac 9Solubility-driven ion parameterization: NaCl as a testbed for multi-scale precipitation/evaporation in porous media Francis Jose, Alexander Schlaich 10 Structure and transport in 2D-nanoconfined electrolytes Damien Toquer, Lydéric Bocquet 10 Excess chemical potential shifts in slit-pore confinement: Definition and calculation using molecular dynamics and free energy methods Kira Fischer, Henrik Stoß, Alexander Schlaich 10 Development and implementation of hybrid atomistic / coarse grained modelling applied to interfaces Hari Haran Sudhakar, Alessandra Serva, Rocio Semino iv 11 Development of a partially reactive force-field for UiO-66 Akanksha Nawani, Rocio Semino 11 Decoding peptide morphologies with topological data analysis Raj Kumar Rajaram Baskaran, Tell Tuttle 12 Towards studying viscoelastic properties of soft magnetic materials using computer simulations Yashas Tejaskumar Gandhi, Christian Holm, Rudolf Weeber 12 Towards agent-based simulations of sarcomere self-assembly with ESPResSo Amirali Zandieh, Abhinav Kumar, Francine Kolley-Köchel, Benjamin M. Friedrich 13 Ion specific implicit solvent coarse-grained model for polyacrylic acid solution Somesh Kurahatti, Svyatoslav Kondrat, David Beyer, Christian Holm 13 Atomistic simulations of polystyrene sulfonate in aqueous solution: effects of water models, force fields, and chemical variations on polymer structure Richard Schömig, Annalena Riffelt 13 A sequence-specific theory for charge-regulating IDPs David Beyer, Christian Holm, Zhen-Gang Wang 14 Interaction models and phase stability of inverse patchy colloids Vanessa Schweidler, Eva G. Noya, Daniele Notarmuzi, Gerhard Kahl, Emanuela Bianchi 14 Charge regulation in polypeptides: experimental approach Ipsita Padhee, Vojtˇ ech Keprta, Sebastian Pineda, Miroslav Štˇ epánek, Peter Košovan 14 Molecular simulation of electroadhesion of polyelectrolyte brushes Magdaléna Nejedlá, Pablo Blanco, Peter Košovan 15 Small-molecules behave as membrane disrupting antibacterial agents Sivadas Palliyil, Harini Sureshkumar, Mintu Porel, Sovan Lal Das, Anand Srivastava Index 22 v Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 Course description Scientific content Multiscale modeling is concerned with simulations of complex physical systems with emergent properties that resolve different timeor length-scales. As a consequence, conflicting goals have to be met. On the one hand, a detailed description at the small scale is needed, but on the other hand, sufficiently long lengthand time-scales have to be covered to capture the problem in its entirety while keeping the computational cost limited. Systematic coarse-graining and machine-learning techniques have become an important tool for bridging the scales: data is collected on accurate but expensive quantum mechanical simulations to generate effective potentials which are suitable for molecular dynamics simulations of much larger systems. This approach helps include quantum effects into simulations at lengthand time-scales that are out of reach for quantum mechanical software. Similarly, resuls from the atomistic scale are used to develop and parametrize coarse-grained models. This school teaches methods for coarse-graining[1], reverse coarse-graining, machine-learning, reinforcement learning, chemical space exploration[2], soft matter physics, and lattice-Boltzmann hydrodynamics [3]. Lectures provide an introduction to the physics and model building of these systems as well as an overview of the necessary simulation algorithms. During the afternoon, students practice running their own simulations in hands-on sessions using ESPResSo[4]. Many of the lectures and hands-on sessions are taught by developers of the software. Hence, the school also provides a platform for discussion between developers and users about the future of the software used in the hands-on sessions. Moreover, users can get advice on their specific simulation projects. Time is also dedicated to research talks, which illustrate how the simulation models and software are applied and provide further background on soft matter at different length and time scales. The poster session opens with lightning talks from all presenters. Teaching material The teaching material used during the school is available online. The ESPResSo software and Jupyter notebooks are free and open-source, available on GitHub (github.com/espressomd/espresso). Lecture slides can be found on the CECAM page for the event (www.cecam.org/workshop-details/1406). Recorded lectures of past iterations of the school are available on the YouTube channel ESPResSo Simulation Package. Hands-on sessions Interactive Jupyter notebooks are used to teach concrete applications of the simulation methods introduced in the lectures. These notebooks outline physical systems relevant to soft matter physics and sketch simulation scripts written for the ESPResSo package using the Python language. These exercises can be carried out in self-study using the web browser via the Binder [5] platform. Software ESPResSo is a free and open-source particle-based simulation package with a focus on coarse-grained molecular dynamics models. In addition, it offers a wide range of schemes for solving electrostatics, magnetostatics, hydrodynamics and electrokinetics, as well as algorithms for active matter and chemical reactions[4,6]. These methods can be combined to simulate different scales and recover emergent material properties at macroscopic scales. In addition, ESPResSo can be coupled to external software to offload calculation of forces using machine-learned potentials, or carry out reinforcement learning to control smart agents in active matter simulations. ESPResSo provides a Python interface which integrates well with scientific packages, such as NumPy [7], pyMBE [8], PyOIF[9], VOTCA[10], ZnDraw[11], and SwarmRL[12]. ESPResSo also leverages HighFive[13] to read/write trajectory files in the portable hdf5 file format [13] with rich metadata using the H5MD specifications [14]. This format facilitates data exchange with other molecular dynamics software and trajectory analysis tools. The organizing committee Tristan Bereau, Christian Holm, Alexander Schlaich, Jean-Noël Grad, Rudolf Weeber (Institute for Theoretical Physics, Heidelberg University, Germany) (Institute for Computational Physics, University of Stuttgart, Germany) (Institute for Physics of Functional Materials, Hamburg University of Technology, Germany) 1 Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 Talks 2 Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 Introduction to machine learning Konstantin Nikolaou1 This lecture aims to build the foundational knowledge required to understand advanced applications of machine learning, such as the development of machine-learned potentials, multi-scale modeling and intelligent active matter. We will build this foundation by first navigating essential terminology and key learning paradigms within the field of machine learning. We will then transition to the practical aspects of model building, followed by a detailed examination of the core components of neural networks. This will cover their architecture, the training process, and their limitations. Ultimately, this lecture provides the direct preparation needed for the specialized follow-up lectures. Structural representations for atomistic machine learning Philip Loche2 The first step in constructing a regression model or performing a data-driven analysis that aims to predict or elucidate the relationship between the atomic-scale structure of matter and its properties is to transform the Cartesian coordinates of particles into a suitable representation, often referred to as a descriptor or fingerprint. The development of atomic-scale representations has played, and continues to play, a central role in the success of machine learning models for chemistry and materials science. This lecture will highlight the current understanding of the nature and characteristics of the most commonly used structural and chemical representations of atomistic systems. We will discuss how atom-centered descriptors are designed for atomistic machine learning, focusing on important ingredients such as smoothness, completeness, symmetry, additivity, and invariance with respect to permutations, translations, and rotations, as well as their connection to equivariant representations. We will begin with physics-inspired descriptors such as the smooth overlap of atomic orbitals (SOAP) and Behler–Parrinello symmetry functions, and then move to modern representations used in graph neural networks, as exemplified by state-of-the-art models like MACE, Allegro, PET, and others. The lecture will emphasize the links between properties, structures, their physical chemistry, and their mathematical description, and will provide examples of recent applications to a diverse range of problems in chemistry and materials science. Machine learning in multiscale modeling for molecular discovery and backmapping Tristan Bereau3 Advanced statistical methods are rapidly impregnating many scientific fields, offering new perspectives on long-standing problems. In materials science, data-driven methods are already bearing fruit in various disciplines, such as hard condensed matter or inorganic chemistry, while comparatively little has happened in soft matter. I will describe how we use multiscale simulations to leverage data-driven methods in soft matter. We aim at establishing structure-property relationships for complex thermodynamic processes across the chemical space of small molecules. Akin to screening experiments, we devise a high-throughput coarse-grained simulation framework. Coarse-graining is an appealing screening strategy for two main reasons: it significantly reduces the size of chemical space and it can suggest a low-dimensional representation of the structure-property relationship. I will describe a biological application of our methodology that led to the discovery of in vivo active compounds. Finally, I will focus on the problem of backmapping, i.e., reconstructing atomistic details from coarse-grained representations. I will present a generative approach conditional on the coarse-grained degrees of freedom. 1Institute for Computational Physics, University of Stuttgart, Germany 2Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland 3Institute for Theoretical Physics, Heidelberg University, Germany 3 Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 Coarse-graining: insights and applications Denis Andrienko1 First, we introduce the key elements of classical particle-based coarse-graining, such as coarse-grained representations, force field basis sets and projection operators[15,16,17]. We will then discuss two distinct projection operators: relative entropy and force matching. Finally, we address the necessity of many-body potentials for predicting the properties of molecular systems. Machine learning potentials: from efficient coarse-grained models to large-scale deployment Julija Zavadlav2 Molecular dynamics simulations are essential for understanding complex phenomena in soft matter physics. A prominent research area is the development of machine learning potentials (MLPs), particularly those based on Graph Neural Networks (GNNs), which have emerged as a powerful tool for bridging the gap between quantummechanical accuracy and classical atomistic or even coarse-grained force field efficiency. In this presentation, I will showcase the significant achievements of both atomistic and coarse-grained MLPs in effectively capturing many-body interactions. I will address the current challenges of MLP development, including the broad and accurate training dataset generation, capturing long-range interactions, and numerical stability. To address these challenges, we propose a range of innovative strategies that encompass synergistic integration of diverse data sources[18,19], novel training objectives[20], physics-based GNN architectures, and advanced Bayesian methods for uncertainty quantification[21]. Through insightful case studies of various molecular systems, I will demonstrate the practical effectiveness and versatility of our approaches. Lastly, I will introduce our software platform, chemtrain[22], designed to streamline the training of machine learning potentials with customizable routines and advanced training algorithms, as well as the extension chemtrain-deploy [23], enabling scalable parallelization across multiple GPUs and million-atom simulations. Reinforcement learning for intelligent active matter Samuel Tovey3 Reinforcement learning is seeing a resurgence, largely due to its recent application in the fine-tuning of large language models. The method has, however, been used over the last two decades to train robots, game-playing computers, and even algorithms for generating molecules, to great success. Recently, attention has slowly shifted towards the application of reinforcement learning in micro-scale active matter, particularly in training agents in simulations to perform interesting tasks. In this talk, we will delve into the foundations of reinforcement learning, examining its evolution and the methods that underpin it, seeing that they extend well beyond the field of artificial intelligence. In the second part of the talk, the research being conducted in Stuttgart using reinforcement learning will be presented and discussed, outlining the current state of the art and introducing avenues for future development. FAIRMD: quality-evaluated all-atom MD trajectories for your model-training pleasure Markus Miettinen4 Building on the NMRlipids Databank[24], the FAIRMD project aims to provide open access to all-atom molecular dynamics trajectories of various biomacromolecular systems (lipid monoand bilayers as well as intrinsically disordered proteins). The trajectories are quality-evaluated against atomistically sensitive experimental data from Nuclear Magnetic Resonance (NMR), Small Angle X-ray Scattering (SAXS), and X-Ray Reflectometry (XRR). Through FAIRMD’s programmable interface, these data are freely available for training and testing of data-driven models. 1Max Planck Institute for Polymer Research, Mainz, Germany 2Multiscale Modeling of Fluid Materials, TUM School of Engineering and Design, Technical University of Munich, Germany 3Institute for Computational Physics, University of Stuttgart, Germany 4University of Bergen, Norway 4 Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 Development of a partially reactive force-field for UiO-66 Akanksha Nawani1, Rocio Semino1 Despite their diverse applications in catalysis, gas storage, biomedicine etc.; the rational design of MetalOrganic Frameworks (MOFs) remains an underexplored field from a mechanistic point of view. To use them to their full potential it is required to understand the mechanics of their formation from the atomic level. One of the MOFs with exceptional stability is UiO-66 which finds its widespread applications owing to its functionalities and tunability through defect engineering. Defects play an important role in tailoring its properties, however, a deeper understanding about the mechanisms that lead to defect formation and role of synthesis conditions is required to advance in rational design strategies. In this poster, I will present a newly developed partially reactive (reactivity only between some components) force-field designed for UiO-66 to model its self-assembly and defect formation. This force-field will allow us to understand the atomistic relationship between synthesis conditions, defect formation and resulting material properties. In addition to it, I present some of the early self-assembly simulations that highlight initial stages of UiO-66 nucleation. Decoding peptide morphologies with topological data analysis Raj Kumar Rajaram Baskaran2, Tell Tuttle2 Self-assembly morphologies such as fibers, sheets, vesicles, and aggregates play a central role in soft matter physics and biomaterials. Traditional geometry-based descriptors often struggle when structures are noisy, distorted, or hybrid, limiting their ability to provide reliable classification. Topological Data Analysis (TDA) [92] offers a robust framework by capturing global features that remain stable under noise, scale, and deformation, making it a promising tool for studying peptide morphologies in molecular simulations. We present a modular pipeline that combines persistent homology, the Euler Characteristic Transform (ECT), and Principal Component Analysis (PCA) for morphology classification. In Stage 1, we benchmarked the method on synthetic point clouds representing five canonical shapes: sphere, vesicle, sheet, tube, and cylinder. Using alpha complexes to compute Betti curves (β0,β1,β2), we observed distinct topological signatures that allowed perfect separation of all five classes. Classification with a Support Vector Machine achieved 100% accuracy across random seeds, and UMAP visualizations confirmed robust clustering. These results establish TDA as a reliable baseline for morphological discrimination in idealized systems. In Stage 2, we extended the pipeline to realistic peptide assemblies by analyzing the 100 largest clusters from diphenylalanine (FF) dipeptide molecular dynamics simulations. To address cluster variability, we applied boundary-weighted farthest point sampling (10%, between 500–2000 points per cluster). We compared topologyonly (ECT), geometry-only (PCA), and hybrid (ECT+PCA) features under noisy and incomplete data. While topology features provided global robustness and PCA captured geometric variance, the hybrid feature set consistently outperformed either approach alone, yielding the most stable and accurate classification. Morphological distributions revealed ~54% fibers, ~18% sheets, and ~22% hybrid or complex aggregates. Temporal tracking of Betti signatures further suggested a morphological evolution pathway, beginning with early sheet-like clusters, transitioning into fibers, and eventually forming complex late-stage aggregates. Our findings demonstrate that TDA-based features provide a noise-resistant and generalizable approach to morphology classification in molecular simulations. By combining ECT with PCA, we enhance sensitivity to subtle structural transitions while retaining robustness under noise and data loss. This hybrid strategy not only benchmarks synthetic shapes but also reveals emergent assembly pathways in peptide systems. Future work will extend the pipeline to multi-component peptide co-assemblies, advancing predictive modeling of biomaterials through robust and interpretable shape classification. 1Sorbonne University, Physico-chimie des Électrolytes et Nanosystèmes Interfaciaux, PHENIX, Paris, France 2University of Strathclyde, Glasgow, United Kingdom 11 Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 Towards studying viscoelastic properties of soft magnetic materials using computer simulations Yashas Tejaskumar Gandhi1, Christian Holm1, Rudolf Weeber1 Magnetic gels combine magnetic and viscoelastic behaviour, with properties governed by both the hydrogel matrix and the embedded magnetic nanoparticles and their interactions. Viscoelasticity can be probed in two main ways: (1) nano-rheology, where an AC magnetic field induces a response analyzed with the Gemant–DiMarzio–Bishop theory [93] to infer local properties; and (2) macroscopic shear experiments measuring stress-strain behaviour. Both methods apply to experiments and simulations, though the latter is computationally demanding. We investigate hydrogel viscoelasticity using the ESPResSo simulation package and a coarse-grained beadspring model that includes hydrodynamic interactions and thermal fluctuations via momentum-conserving dissipative particle dynamics. Our current goal is to parametrize the model to match key experimental hydrogel features. Future work will incorporate magnetic nanoparticles to examine how their constraints and dipole-dipole interactions affect local deformation. Towards agent-based simulations of sarcomere self-assembly with ESPResSo Amirali Zandieh2, Abhinav Kumar2, Francine Kolley-Köchel2, Benjamin M. Friedrich2 Walking, flying, and heartbeat are driven by micrometer-sized sarcomeres, the elementary contractile units of striated muscle. Sarcomeres are arranged in series into periodic arrays known as myofibrils, which extend across the entire length of a muscle cell. While the molecular structure of mature sarcomeres is well understood, the physical principles by which these structures emerge during development remain open. Work on Drosophila indirect flight muscle has shown that the molecular components, myosin motor filaments, Z-disk proteins, and the giant protein titin/Sallimus, assemble into periodic patterns first, while actin filaments become polarity sorted only later. This observation motivated the development of minimal models[94], accounting for non-local interactions between extended filaments and proposing two possible mechanisms: sarcomeric pattern formation mediated by the giant protein titin and tension-responsive catch bonds. Both models reproduce key experimental observations in one-dimensional agent-based simulations. To capture higher-dimensional aspects of myofibrillogenesis, such as cross-sectional organization, we implemented a proof-of-concept extension of these models in two and three dimensions using the ESPResSo simulation package. In our coarse-grained, agent-based approach, myosin and Z-disk complexes are represented as particles coupled through titin-like virtual links. To account for the reversible nature of binding during sarcomere assembly, we introduced dynamic type switching upon bond formation or breakage, so particles can change state over successive attachment cycles. In addition, we implemented an anisotropic custom potential to better reflect the geometry of elongated filaments. We demonstrate that sarcomere-like periodic arrangements emerge and become registered in-phase across parallel chains, as characterized by order kuramoto parameters previously developed for synchronization phenomena. Walking, flying, and heartbeat are driven by micrometer-sized sarcomeres, the elementary contractile units of striated muscle. Sarcomeres are arranged in series into periodic arrays known as myofibrils, which extend across the entire length of a muscle cell. While the molecular structure of mature sarcomeres is well understood, the physical principles by which these structures emerge during development remain open. Our current work focuses on non-local interactions mediated by titin. Ultimately, we aim to extend this framework to also include feedback from local mechanical tension. Our initial results suggest that agent-based simulations can complement minimal models and provide a more realistic route toward understanding the physical basis of sarcomere self-assembly, and more generally meso-scale pattern formation of functional cytoskeletal structures. 1Institute for Computational Physics, University of Stuttgart, Germany 2Cluster of Excellence Physics of Life, Technical University Dresden, Germany 12 Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 Ion specific implicit solvent coarse-grained model for polyacrylic acid solution Somesh Kurahatti1, Svyatoslav Kondrat1,2, David Beyer1, Christian Holm1 A systematic molecular coarse-graining (CG) approach for aqueous polyelectrolyte solutions is presented with an atactic poly-acrylic acid (PAA) chain. The acrylate repeat unit is mapped on a one-site CG bead representation with the counterion being modeled explicitly while the solvent is modeled implicitly[95]. The coarse-grained bonded and nonbonded potentials are developed independently. Bonded interactions (bond, angle, dihedral) are obtained by Boltzmann inversion of distributions from in-vacuo atomistic chain simulations. Nonbonded bead– bead, bead–ion, and ion–ion interactions are derived as potentials of mean force from umbrella sampling in explicit water for various monovalent counterions (Na+, K+, Cs+). The CG model reproduces the ion-specific structural and conformational properties of polyelectrolyte chains in good agreement with the parent atomistic chains in aqueous solution. We also carried out CG simulation at various chain lengths to assess the transferability of CG potentials to various polymer concentrations. Furthermore, this model can enable us to perform large-scale simulations of polymer networks to investigate ion-specific swelling and mechanical properties. Atomistic simulations of polystyrene sulfonate in aqueous solution: effects of water models, force fields, and chemical variations on polymer structure Richard Schömig3, Annalena Riffelt4 Polystyrene sulfonate (PSS) is often regarded as a model polyelectrolyte in literature due to its strongly acidic sulfonate groups. However, its actual chemical structure can vary with the synthesis route, and deviations from the idealized, fully sulfonated backbone may significantly influence properties such as solubility and chain conformation. Here, we use atomistic molecular dynamics simulations to investigate how structural variations and modeling choices affect PSS in aqueous solution. We explore different water models, force-field parameters, chain lengths, degrees of protonation, tacticity, and counterion types, as well as substitution patterns (ortho/meta positioning) and unsubstituted styrene units. By analyzing structural descriptors such as the radius of gyration and persistence length, we expect to identify how subtle chemical differences and model parameterization shape polymer conformations, thereby guiding a more realistic description of PSS in solution. A sequence-specific theory for charge-regulating IDPs David Beyer1, Christian Holm1, Zhen-Gang Wang5 Predicting the sequence-specific conformational behavior of intrinsically disordered proteins (IDPs) is a complex issue that necessitates going beyond the classical scaling theories for random polyampholytes. A particularly challenging problem is the prediction of charge-regulation effects in these systems, i.e. the inclusion of acid-base equilibria of the amino acids residues. Here, we develop a sequence-specific variational theory for charge-regulating IPDs. Our theory generalizes the seminal work of Sawle and Ghosh[96] to the constant-pH ensemble, allowing us to take into account the residue-specific ionization state. Like the earlier work on quenched IDPs, our theory is based on the Edwards–Singh variational approach[97], which yields a set of self-consistent equations for the effective chain size and residue-specific mean-fields. We numerically demonstrate that our theory can predict non-trivial effects like the non-uniform backbone charge distribution along weak polyelectrolytes and sequence-specific swelling and ionization in weak polyampholytes. 1Institute for Computational Physics, University of Stuttgart, Germany 2Institute of Physical Chemistry, Polish Academy of Sciences, Warsaw, Poland 3Institute for Physics of Functional Materials, Hamburg University of Technology, Germany 4SC SimTech, University of Stuttgart, Germany 5Division of Chemistry and Chemical Engineering, California Institute of Technology, USA 13 Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 Interaction models and phase stability of inverse patchy colloids Vanessa Schweidler1, Eva G. Noya2, Daniele Notarmuzi1, Gerhard Kahl1, Emanuela Bianchi1 Colloidal particles with heterogeneous surface charge patterns exhibit anisotropic electrostatic interactions, leading to a complex interplay of attraction and repulsion that governs their large-scale self-organization. Recent experimental studies have demonstrated that such charge anisotropy can be engineered to guide self-assembly in colloidal systems[98]. To explore these effects systematically, we employ a coarse-grained model: the inverse patchy colloid (IPC) framework [99]. The effective pair potential is defined as a sum over site–site interactions of the form UAB (r,Ω)=∑︂ α,β εαβ ·ωαβ (rαβ ), where εαβ are characteristic interaction strengths and ωαβ are geometric weight functions depending on inter-site distances and orientations. The indices AB refer to a specific particle configuration. In previous IPC models, ωαβ was based on the overlap of spherical interaction volumes [99]. In our current work, we introduce a Yukawa form of the weight function, ωαβ (rαβ )=exp (︁−κ·rαβ )︁/rαβ , where κis the inverse screening length. This formulation more realistically captures screened electrostatic interactions and refines the “exponential” model introduced in a recent generalization of the IPC framework [100]. We combine Monte Carlo and Molecular Dynamics simulations with free energy calculation methods[101, 102] to investigate how this refinement affects the predicted phase behavior. By computing and comparing the free energies of systems with identical geometric parameters under both interaction models, we aim to quantify the impact of the underlying electrostatic modeling on fluid–solid and solid–solid equilibrium. These insights advance the predictive design of self-assembling, anisotropically charged colloids for applications in soft matter and materials science. The computational results are obtained using the Vienna Scientific Cluster VSC (VSC). The authors acknowledge support from the Austrian Science Fund (FWF) under Project No. Y-1163-N27. Charge regulation in polypeptides: experimental approach Ipsita Padhee3, Vojtˇ ech Keprta3, Sebastian Pineda3, Miroslav Štˇ epánek3, Peter Košovan3 Understanding the pH-dependent behavior of weak polyelectrolytes such as polypeptides is essential, as charge regulation affects their structure, stability, and function. We investigated the ionization of model polypeptides containing weakly acidic and basic residues by measuring its degree of ionization as a function of pH using potentiometric titration and fluorimetry, complemented by coarse-grained simulations. Experimental and computational results both followed the general trend predicted by the Henderson–Hasselbalch equation, though deviations arises from electrostatic interactions. Overall, the simulations reliably captured qualitative charge regulation behavior, while further model refinements are needed for quantitative accuracy. Molecular simulation of electroadhesion of polyelectrolyte brushes Magdaléna Nejedlá3, Pablo Blanco4, Peter Košovan3 Electroadhesion occurs between otherwise non-adhesive hydrogels: one cationic and the other anionic. After the application of an electric field (10 V) for several seconds, the hydrogels adhere to each other. The adhesion persists until an electric field of opposite polarity is applied. Despite very promising applications, the molecular mechanism of electroadhesion is not yet understood. This lack of understanding limits the possibilities of rational choice of suitable hydrogel materials for electroadhesion to various surfaces. 1Institute for Theoretical Physics, Technical University of Vienna, Austria 2Instituto de Química Física “Blas Cabrera”, CSIC, Madrid, Spain 3Department of Physical and Macromolecular Chemistry, Faculty of Science, Charles University, Prague, Czech Republic 4Department of Physics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway 14 Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 Small-molecules behave as membrane disrupting antibacterial agents Sivadas Palliyil1, Harini Sureshkumar2, Mintu Porel3, Sovan Lal Das4, Anand Srivastava2 We present a modular platform based on sequence-defined oligomers (SDOs) to address the constraints of antimicrobial peptides while retaining their benefits. SDOs are a class of oligomers in which the sequence of monomers is precisely controlled on a backbone. A library of these small molecules (having a molar mass less than 350Da) was synthesized by changing the hydrophobic part (aliphatic, cyclic, and aromatic groups) while keeping the cationic site unchanged. Dec and Oct exhibit promising antibacterial activity among all the SDOs towards B. subtilis and E. coli bacteria. The mode of action of the SDOs was elucidated on a model system, where bacterial membranes mimicking giant unilamellar vesicles (GUVs) were exposed to the SDOs. Membrane disruption and pore formation were found to be the key mechanisms of killing bacteria. Importantly, the hemolysis assay results indicated a high degree of selectivity of the SDOs toward bacterial cells than red blood cells (RBCs). To understand the reason behind the less activity to RBCs, the interaction of SDOs with GUVs containing different compositions of DOPC, DPPC and cholesterol has been employed. Membranes of such GUVs are thought to be closer to that of erythrocytes in composition. Cholesterol content inside the GUVs only increases the pore formation. Aromatic side chained SDOs (Bnz) cause much less disruption and high pore formation on DOPC:Chol vesicles as compared to linear side chained SDOs. DOPC, DOPG lipids are more vulnerable to these small molecules than the DPPC lipids. Molecular dynamics simulations can also be employed to have a deeper understanding of these mechanisms of action of SDOs. First we developed CHARMM [103] compatible forcefields for this new class of small molecules. The validation of the forcefields are assessed by comparing the IR spectrum calculated from MD simulations with the experimental FTIR spectrum. We extended the forcefields into the coarse-grained Martini 3[104] framework as well. 1Department of Physics & Physical and Chemical Biology Lab, Indian Institute of Technology Palakkad, India 2Molecular Biophysics Unit, Indian Institute of Science, Banglore, India 3Department of Chemistry, Indian Institute of Technology Palakkad, India 4Department of Mechanical Engineering & Physical and Chemical Biology Lab, Indian Institute of Technology Palakkad, India 15 Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 Bibliography [1] Christoph Scherer, René Scheid, Denis Andrienko, and Tristan Bereau. “Kernel-based machine learning for efficient simulations of molecular liquids”. In: Journal of Chemical Theory and Computation 16.5 (Apr. 2020), pp. 3194–3204. ISSN: 1549-9626. DOI:10.1021/acs.jctc.9b01256. [2] Roberto Menichetti, Kiran H. Kanekal, and Tristan Bereau. “Drug–membrane permeability across chemical space”. In: ACS Central Science 5.2 (Jan. 2019), pp. 290–298. ISSN: 2374-7951. DOI:10.1021/acscentsci. 8b00718. [3] Timm Krüger, Halim Kusumaatmaja, Alexandr Kuzmin, Orest Shardt, Goncalo Silva, and Erlend Magnus Viggen. The lattice Boltzmann method: principles and practice. Graduate Texts in Physics. Cham, Switzerland: Springer International Publishing, 2017. ISBN: 978-3-319-44649-3. DOI:10.1007/978-3-319-44649-3. [4] Florian Weik, Rudolf Weeber, Kai Szuttor, Konrad Breitsprecher, Joost de Graaf, Michael Kuron, Jonas Landsgesell, Henri Menke, David Sean, and Christian Holm. “ESPResSo 4.0 – an extensible software package for simulating soft matter systems”. In: European Physical Journal Special Topics 227.14 (2019): Particle Methods in Natural Science and Engineering, pp. 1789–1816. DOI:10.1140/epjst/e2019-800186-9. [5] Project Jupyter et al. “Binder 2.0 - reproducible, interactive, sharable environments for science at scale”. In: Proceedings of the 17th Python in Science Conference. Ed. by Fatih Akici, David Lippa, Dillon Niederhut, and M Pacer. 2018, pp. 113–120. DOI:10.25080/Majora-4af1f417-011. [6] Rudolf Weeber, Jean-Noël Grad, David Beyer, Pablo M. Blanco, Patrick Kreissl, Alexander Reinauer, Ingo Tischler, Peter Košovan, and Christian Holm. “ESPResSo, a versatile open-source software package for simulating soft matter systems”. In: Comprehensive Computational Chemistry. Ed. by Manuel Yáñez and Russell J. Boyd. 1st edition. Oxford: Elsevier, 2024, pp. 578–601. ISBN: 978-0-12-823256-9. DOI:10.1016/ B978-0-12-821978-2.00103-3. [7] Charles R. Harris et al. “Array programming with NumPy”. In: Nature 585.7825 (Sept. 2020), pp. 357–362. DOI:10.1038/s41586-020-2649-2. [8] David Beyer, Paola B. Torres, Sebastian P. Pineda, Claudio F. Narambuena, Jean-Noël Grad, Peter Košovan, and Pablo M. Blanco. “pyMBE: the Python-based molecule builder for ESPResSo”. In: The Journal of Chemical Physics 161.2 (Modular and Interoperable Software for Chemical Physics July 2024), p. 022502. DOI:10.1063/5.0216389. [9] Iveta Janˇ cigová, Kristína Kovalˇ cíková, Rudolf Weeber, and Ivan Cimrák. “PyOIF: computational tool for modelling of multi-cell flows in complex geometries”. In: PLoS Computational Biology 16.10 (2020), e1008249. DOI:10.1371/journal.pcbi.1008249. [10] S. Y. Mashayak, Mara N. Jochum, Konstantin Koschke, N. R. Aluru, Victor Rühle, and Christoph Junghans. “Relative entropy and optimization-driven coarse-graining methods in VOTCA”. In: PLOS ONE 10.7 (July 2015), pp. 1–20. DOI:10.1371/journal.pone.0131754. [11] Rokas Elijošius, Fabian Zills, Ilyes Batatia, Sam Walton Norwood, Dávid Péter Kovács, Christian Holm, and Gábor Csányi. “Zero shot molecular generation via similarity kernels”. In: Nature Communications 16.1 (July 2025), p. 5991. DOI:10.1038/s41467-025-60963-3. [12] Samuel Tovey, Christoph Lohrmann, Tobias Merkt, David Zimmer, Konstantin Nikolaou, Simon Koppenhöfer, Anna Bushmakina, Jonas Scheunemann, and Christian Holm. “SwarmRL: building the future of smart active systems”. In: The European Physical Journal E 48.4–5 (Apr. 2025). ISSN: 1292-895X. DOI: 10.1140/epje/s10189-025-00477-4. [13] Adrien Devresse, Nicolas Cornu, Luc Grosheintz-Laval, Omar Awile, Tom de Geus, Fernando Pereira, Matthias Wolf, and HighFive Contributors. HighFive - header-only C++ HDF5 interface. Version v2.10.1. Dec. 2024. DOI:10.5281/zenodo.14272664. 16 Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 [14] Pierre de Buyl, Peter H. Colberg, and Felix Höfling. “H5MD: A structured, efficient, and portable file format for molecular data”. In: Computer Physics Communications 185.6 (2014), pp. 1546–1553. ISSN: 0010-4655. DOI:10.1016/j.cpc.2014.01.018. [15] W. G. Noid. “Perspective: advances, challenges, and insight for predictive coarse-grained models”. In: The Journal of Physical Chemistry B 127.19 (May 2023), pp. 4174–4207. ISSN: 1520-5207. DOI:10.1021/acs. jpcb.2c08731. [16] Victor Rühle, Christoph Junghans, Alexander Lukyanov, Kurt Kremer, and Denis Andrienko. “Versatile object-oriented toolkit for coarse-graining applications”. In: Journal of Chemical Theory and Computation 5.12 (Nov. 2009), pp. 3211–3223. ISSN: 1549-9626. DOI:10.1021/ct900369w. [17] Christoph Scherer, Naomi Kinaret, Kun-Han Lin, Muhammad Nawaz Qaisrani, Felix Post, Falk May, and Denis Andrienko. “Predicting molecular ordering in deposited molecular films”. In: Advanced Energy Materials 14.44 (Sept. 2024), p. 2403124. ISSN: 1614-6840. DOI:10.1002/aenm.202403124. [18] Stephan Thaler and Julija Zavadlav. “Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting”. In: Nature Communications 12.1 (Nov. 2021). ISSN: 2041-1723. DOI: 10.1038/s41467-021-27241-4. [19] Sebastien Röcken, Anton F. Burnet, and Julija Zavadlav. “Predicting solvation free energies with an implicit solvent machine learning potential”. In: The Journal of Chemical Physics 161.23 (Dec. 2024). ISSN: 10897690. DOI:10.1063/5.0235189. [20] Stephan Thaler, Maximilian Stupp, and Julija Zavadlav. “Deep coarse-grained potentials via relative entropy minimization”. In: The Journal of Chemical Physics 157.24 (Dec. 2022), p. 244103. ISSN: 1089-7690. DOI: 10.1063/5.0124538. [21] Stephan Thaler, Gregor Doehner, and Julija Zavadlav. “Scalable Bayesian uncertainty quantification for neural network potentials: promise and pitfalls”. In: Journal of Chemical Theory and Computation 19.14 (Apr. 2023), pp. 4520–4532. ISSN: 1549-9626. DOI:10.1021/acs.jctc.2c01267. [22] Paul Fuchs, Stephan Thaler, Sebastien Röcken, and Julija Zavadlav. “chemtrain: learning deep potential models via automatic differentiation and statistical physics”. In: Computer Physics Communications 310 (May 2025), p. 109512. ISSN: 0010-4655. DOI:10.1016/j.cpc.2025.109512. [23] Paul Fuchs, Weilong Chen, Stephan Thaler, and Julija Zavadlav. “chemtrain-deploy: a parallel and scalable framework for machine learning potentials in million-atom MD simulations”. In: Journal of Chemical Theory and Computation 21.15 (July 2025), pp. 7550–7560. ISSN: 1549-9626. DOI:10.1021/acs.jctc.5c00996. [24] Anne M. Kiirikki et al. “Overlay databank unlocks data-driven analyses of biomolecules for all”. In: Nature Communications 15.1 (Feb. 2024). ISSN: 2041-1723. DOI:10.1038/s41467-024-45189-z. [25] Mathias Schreiner, Ole Winther, and Simon Olsson. “Implicit transfer operator learning: multiple timeresolution models for molecular dynamics”. In: Advances in Neural Information Processing Systems. 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (New Orleans, Louisiana, USA, Dec. 10–16, 2023). Ed. by A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine. Vol. 36. Curran Associates, Inc., 2023, pp. 36449–36462. ISBN: 9781713899921. URL:https://proceedings.neurips. cc/paper_files/paper/2023/hash/7274ed909a312d4d869cc328ad1c5f04-Abstract-Conference.html. [26] Juan Viguera Diez, Mathias Schreiner, Ola Engkvist, and Simon Olsson. Boltzmann priors for implicit transfer operators. ArXiv preprint 2410.10605. Apr. 2025. arXiv: 2410.10605 [physics.chem-ph]. [27] Pierre-Gilles de Gennes. Scaling concepts in polymer physics. Ithaca, New-York, USA: Cornell University Press, Dec. 1979. ISBN: 0-8014-1203-X. URL:https://www.cornellpress.cornell.edu/book/9780801412035/ scaling-concepts-in-polymer-physics. [28] Kurt Kremer and Gary S. Grest. “Dynamics of entangled linear polymer melts: a molecular-dynamics simulation”. In: The Journal of Chemical Physics 92.8 (Apr. 1990), pp. 5057–5086. ISSN: 1089-7690. DOI: 10.1063/1.458541. [29] Hans-Jürgen Butt, Karlheinz Graf, and Michael Kappl. “Physics and chemistry of interfaces”. In: Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2003. Chap. 4: The electric double layer, pp. 42–56. ISBN: 9783527602315. DOI:10.1002/3527602313.ch4. [30] P. Debye and E. Hückel. “Zur Theorie der Elektrolyte. I. Gefrierpunktserniedrigung und verwandte Erscheinungen”. In: Physikalische Zeitschrift 24.9 (1923), pp. 185–206. [31] M. Gouy. “Sur la constitution de la charge électrique à la surface d’un électrolyte”. In: Journal de Physique Théorique et Appliquée 9.1 (1910), pp. 457–468. DOI:10.1051/jphystap:019100090045700. 17 Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 [32] David Leonard Chapman. “A contribution to the theory of electrocapillarity”. In: Philosophical Magazine 25.148 (Apr. 1913), pp. 475–481. DOI:10.1080/14786440408634187. [33] David C. Grahame. “The electrical double layer and the theory of electrocapillarity”. In: Chemical Reviews 41.3 (1947), pp. 441–501. DOI:10.1021/cr60130a002. [34] B. V. Derjaguin and L. D. Landau. “Theory of the stability of strongly charged lyophobic sols and of the adhesion of strongly charged particles in solutions of electrolytes”. In: Acta Physicochimica (USSR) 14 (1941), pp. 633–650. [35] E. J. Verwey and J. Th. G. Overbeek. Theory of the stability of lyophobic colloids: the interaction of sol particles having an electric double layer. Leiden, Netherlands: Elsevier, 1948. [36] Axel Arnold et al. “Comparison of scalable fast methods for long-range interactions”. In: Physical Review E 88.6 (Dec. 2013), p. 063308. DOI:10.1103/PhysRevE.88.063308. [37] P. P. Ewald. “Die Berechnung optischer und elektrostatischer Gitterpotentiale”. In: Annalen der Physik 369.3 (1921), pp. 253–287. DOI:10.1002/andp.19213690304. [38] Markus Deserno and Christian Holm. “How to mesh up Ewald sums. I. A theoretical and numerical comparison of various particle mesh routines”. In: The Journal of Chemical Physics 109 (Nov. 1998), p. 7678. DOI: 10.1063/1.477414. [39] Markus Deserno and Christian Holm. “How to mesh up Ewald sums. II. An accurate error estimate for the Particle–Particle–Particle-Mesh algorithm”. In: The Journal of Chemical Physics 109 (Nov. 1998), p. 7694. DOI:10.1063/1.477415. [40] In-Chul Yeh and Max L. Berkowitz. “Ewald summation for systems with slab geometry”. In: The Journal of Chemical Physics 111.7 (Aug. 1999), pp. 3155–3162. DOI:10.1063/1.479595. [41] Axel Arnold, Jason de Joannis, and Christian Holm. “Electrostatics in periodic slab geometries. I”. In: The Journal of Chemical Physics 117.6 (Aug. 2002), pp. 2496–2502. DOI:10.1063/1.1491955. [42] Jason de Joannis, Axel Arnold, and Christian Holm. “Electrostatics in periodic slab geometries. II”. In: The Journal of Chemical Physics 117.6 (Aug. 2002), pp. 2503–2512. DOI:10.1063/1.1491954. [43] Sandeep Tyagi, Axel Arnold, and Christian Holm. “ICMMM2D: an accurate method to include planar dielectric interfaces via image charge summation”. In: The Journal of Chemical Physics 127.15 (Oct. 2007), p. 154723. DOI:10.1063/1.2790428. [44] Sandeep Tyagi, Axel Arnold, and Christian Holm. “Electrostatic layer correction with image charges: a linear scaling method to treat slab 2D + h systems with dielectric interfaces”. In: The Journal of Chemical Physics 129.20 (2008), p. 204102. DOI:10.1063/1.3021064. [45] Sandeep Tyagi, Mehmet Süzen, Marcello Sega, Marcia C. Barbosa, Sofia S. Kantorovich, and Christian Holm. “An iterative, fast, linear-scaling method for computing induced charges on arbitrary dielectric boundaries”. In: The Journal of Chemical Physics 132.15 (2010), p. 154112. DOI:10.1063/1.3376011. [46] Axel Arnold, Konrad Breitsprecher, Florian Fahrenberger, Stefan Kesselheim, Olaf Lenz, and Christian Holm. “Efficient algorithms for electrostatic interactions including dielectric contrasts”. In: Entropy 15.11 (2013), pp. 4569–4588. DOI:10.3390/e15114569. [47] Konrad Breitsprecher, Christian Holm, and Svyatoslav Kondrat. “Charge me slowly, I am in a hurry: optimizing charge–discharge cycles in nanoporous supercapacitors”. In: ACS Nano 12.10 (Aug. 2018), pp. 9733– 9741. DOI:10.1021/acsnano.8b04785. [48] Peter Košovan, Jitka Kuldová, Zuzana Limpouchová, Karel Procházka, Ekaterina B. Zhulina, and Oleg V. Borisov. “Molecular dynamics simulations of a polyelectrolyte star in poor solvent”. In: Soft Matter 6.9 (2010), pp. 1872–1874. DOI:10.1039/B925067K. [49] Jonas Landsgesell, David Beyer, Pascal Hebbeker, Peter Košovan, and Christian Holm. “The pH-dependent swelling of weak polyelectrolyte hydrogels modeled at different levels of resolution”. In: Macromolecules 55.8 (Feb. 2022), pp. 3176–3188. DOI:10.1021/acs.macromol.1c02489. [50] David Beyer and Christian Holm. “Unexpected two-stage swelling of weak polyelectrolyte brushes with divalent counterions”. In: ACS Macro Letters 13.9 (2024), pp. 1185–1191. DOI:10 .1021 /acsmacrolett. 4c00421. [51] Kai Szuttor, Florian Weik, Jean-Noël Grad, and Christian Holm. “Modeling the current modulation of bundled DNA structures in nanopores”. In: The Journal of Chemical Physics 154.5 (2021), p. 054901. DOI:10.1063/ 5.0038530. 18 Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 [52] Kai Szuttor, Patrick Kreissl, and Christian Holm. “A numerical investigation of analyte size effects in nanopore sensing systems”. In: The Journal of Chemical Physics 155.13 (2021), p. 134902. DOI:10.1063/5.0065085. [53] G. Inci, A. Kronenburg, R. Weeber, and D. Pflüger. “Langevin dynamics simulation of transport and aggregation of soot nano-particles in turbulent flows”. In: Flow, Turbulence and Combustion 98.4 (Jan. 2017), pp. 1065–1085. DOI:10.1007/s10494-016-9797-3. [54] Benedict J. Reynwar, Gregoria Illya, Vagelis A. Harmandaris, Martin M. Müller, Kurt Kremer, and Markus Deserno. “Aggregation and vesiculation of membrane proteins by curvature-mediated interactions”. In: Nature 447.7143 (May 2007), pp. 461–464. DOI:10.1038/nature05840. [55] Miru Lee, Christoph Lohrmann, Kai Szuttor, Harold Auradou, and Christian Holm. “The influence of motility on bacterial accumulation in a microporous channel”. In: Soft Matter 17.4 (2021), pp. 893–902. DOI:10. 1039/d0sm01595d. [56] Kenneth Hoste, Jens Timmerman, Andy Georges, and Stijn De Weirdt. “EasyBuild: building software with ease”. In: Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis (Salt Lake City, Utah, USA, Nov. 10–16, 2012). Los Alamitos, California, USA: IEEE, Nov. 2012. ISBN: 978-0-7695-4956-9. DOI:10.1109/SC.Companion.2012.81. [57] Markus Geimer, Kenneth Hoste, and Robert McLay. “Modern scientific software management using EasyBuild and Lmod”. In: 2014 First International Workshop on HPC User Support Tools (Nov. 21–21, 2014). 2014, pp. 41–51. ISBN: 978-1-4673-6755-4. DOI:10.1109/HUST.2014.8. [58] Bob Dröge, Victor Holanda Rusu, Kenneth Hoste, Caspar van Leeuwen, Alan O’Cais, and Thomas Röblitz. “EESSI: a cross-platform ready-to-use optimised scientific software stack”. In: Software: Practice and Experience 53.1 (Jan. 2023), pp. 176–210. DOI:10.1002/spe.3075. [59] P. L. Bhatnagar, E. P. Gross, and M. Krook. “A model for collision processes in gases. I. Small amplitude processes in charged and neutral one-component systems”. In: Physical Review 94.3 (May 1954), pp. 511– 525. DOI:10.1103/physrev.94.511. [60] Vladimir Lobaskin and Burkhard Dünweg. “A new model for simulating colloidal dynamics”. In: New Journal of Physics 6 (May 2004), pp. 54–54. DOI:10.1088/1367-2630/6/1/054. [61] Lukas P. Fischer, Toni Peter, Christian Holm, and Joost de Graaf. “The raspberry model for hydrodynamic interactions revisited. I. Periodic arrays of spheres and dumbbells”. In: The Journal of Chemical Physics 143.8 (Aug. 2015). DOI:10.1063/1.4928502. [62] Joost de Graaf, Toni Peter, Lukas P. Fischer, and Christian Holm. “The raspberry model for hydrodynamic interactions revisited. II. The effect of confinement”. In: The Journal of Chemical Physics 143.8 (Aug. 2015). DOI:10.1063/1.4928503. [63] Alan K. Todd, Matthew Johnston, and Stephen Neidle. “Highly prevalent putative quadruplex sequence motifs in human DNA”. In: Nucleic Acids Research 33.9 (Jan. 2005), pp. 2901–2907. ISSN: 0305-1048. DOI: 10.1093/nar/gki553. [64] Sofia Kolesnikova and Edward A. Curtis. “Structure and function of multimeric G-quadruplexes”. In: Molecules 24.17 (Aug. 2019), p. 3074. ISSN: 1420-3049. DOI:10.3390/molecules24173074. [65] Giulia Biffi, David Tannahill, John McCafferty, and Shankar Balasubramanian. “Quantitative visualization of DNA G-quadruplex structures in human cells”. In: Nature Chemistry 5.3 (Jan. 2013), pp. 182–186. ISSN: 1755-4349. DOI:10.1038/nchem.1548. [66] Julian L. Huppert and Shankar Balasubramanian. “G-quadruplexes in promoters throughout the human genome”. In: Nucleic Acids Research 35.2 (Dec. 2006), pp. 406–413. ISSN: 0305-1048. DOI:10.1093/nar/ gkl1057. [67] Alan M. Zahler, James R. Williamson, Thomas R. Cech, and David M. Prescott. “Inhibition of telomerase by G-quartet DNA structures”. In: Nature 350.6320 (Apr. 1991), pp. 718–720. ISSN: 1476-4687. DOI:10.1038/ 350718a0. [68] Qian Li, Jun-Feng Xiang, Qian-Fan Yang, Hong-Xia Sun, Ai-Jiao Guan, and Ya-Lin Tang. “G4LDB: a database for discovering and studying G-quadruplex ligands”. In: Nucleic Acids Research 41.D1 (Nov. 2012), pp. D1115– D1123. ISSN: 1362-4962. DOI:10.1093/nar/gks1101. [69] Stephen Neidle. “Quadruplex nucleic acids as targets for anticancer therapeutics”. In: Nature Reviews Chemistry 1.5 (May 2017). ISSN: 2397-3358. DOI:10.1038/s41570-017-0041. 19 Book of Abstracts of the 2025 ESPResSo Summer School, Stuttgart, Germany, October 6–10, 2025 [70] Deniz Mostarac, Mattia Trapella, Luca Bertini, Lucia Comez, Alessandro Paciaroni, and Cristiano De Michele. “Polymeric properties of telomeric G-quadruplex multimers: effects of chemically inert crowders”. In: Biomacromolecules 26.5 (Apr. 2025), pp. 3128–3138. ISSN: 1526-4602. DOI:10.1021/acs.biomac.5c00176. [71] Fabian Zills, Sheena Agarwal, Tiago J Goncalves, Srishti Gupta, Edvin Fako, Shuang Han, Imke Britta Mueller, Christian Holm, and Sandip De. “MLIPX: Machine Learned Interatomic Potential eXploration”. In: Journal of Physics: Condensed Matter 37.38 (Sept. 2025), p. 385901. ISSN: 1361-648X. DOI:10.1088/1361648x/ae0111. [72] Reto Strobel and Sotiris E. Pratsinis. “Flame aerosol synthesis of smart nanostructured materials”. In: Journal of Materials Chemistry 17.45 (2007), p. 4743. ISSN: 1364-5501. DOI:10.1039/b711652g. [73] Renyi Zhang, Alexei F. Khalizov, Joakim Pagels, Dan Zhang, Huaxin Xue, and Peter H. McMurry. “Variability in morphology, hygroscopicity, and optical properties of soot aerosols during atmospheric processing”. In: Proceedings of the National Academy of Sciences 105.30 (July 2008), pp. 10291–10296. ISSN: 1091-6490. DOI:10.1073/pnas.0804860105. [74] J. Morán, A. Fuentes, F. Liu, and J. Yon. “FracVAL: an improved tunable algorithm of cluster–cluster aggregation for generation of fractal structures formed by polydisperse primary particles”. In: Computer Physics Communications 239 (June 2019), pp. 225–237. ISSN: 0010-4655. DOI:10.1016/j.cpc.2019.01.015. [75] G. Inci, A. Arnold, A. Kronenburg, and R. Weeber. “Modeling nanoparticle agglomeration using local interactions”. In: Aerosol Science and Technology 48.8 (July 2014), pp. 842–852. ISSN: 1521-7388. DOI: 10.1080/02786826.2014.932942. [76] J. Yon, J. Morán, F.-X. Ouf, M. Mazur, and J. B. Mitchell. “From monomers to agglomerates: a generalized model for characterizing the morphology of fractal-like clusters”. In: Journal of Aerosol Science 151 (Jan. 2021), p. 105628. ISSN: 0021-8502. DOI:10.1016/j.jaerosci.2020.105628. [77] Majid Beidaghi and Yury Gogotsi. “Capacitive energy storage in micro-scale devices: recent advances in design and fabrication of micro-supercapacitors”. In: Energy & Environmental Science 7.3 (2014), p. 867. ISSN: 1754-5706. DOI:10.1039/c3ee43526a. [78] Jens Both. “The modern era of aluminum electrolytic capacitors”. In: IEEE Electrical Insulation Magazine 31.4 (July 2015), pp. 24–34. ISSN: 0883-7554. DOI:10.1109/mei.2015.7126071. [79] Ivan Palaia. “Charged systems in, out of, and driven to equilibrium: from nanocapacitors to cement”. PhD thesis. Université Paris Saclay (COmUE), Nov. 2019. URL:https://theses.hal.science/tel-02926717. [80] Ivan Palaia, Adelchi J. Asta, Megh Dutta, Patrick B. Warren, Benjamin Rotenberg, and Emmanuel Trizac. “Charging dynamics of electric double-layer nanocapacitors in mean field”. In: Physical Review Letters 135.14 (Sept. 2025), p. 148002. DOI:10.1103/72b9-c8cq. [81] Ivan Palaia, Adelchi J. Asta, Megh Dutta, Patrick B. Warren, Benjamin Rotenberg, and Emmanuel Trizac. “Poisson–Nernst–Planck charging dynamics of an electric double-layer capacitor: symmetric and asymmetric binary electrolytes”. In: Physical Review E 112.3 (Sept. 2025), p. 035417. DOI:10.1103/p4dg-snqf. [82] Eveline Rigo, Zhuxin Dong, Jae Hyun Park, Eamonn Kennedy, Mohammad Hokmabadi, Lisa AlmonteGarcia, Li Ding, Narayana Aluru, and Gregory Timp. “Measurements of the size and correlations between ions using an electrolytic point contact”. In: Nature Communications 10.1 (May 2019). ISSN: 2041-1723. DOI:10.1038/s41467-019-10265-2. [83] Ingo Tischler, Florian Weik, Robert Kaufmann, Michael Kuron, Rudolf Weeber, and Christian Holm. “A thermalized electrokinetics model including stochastic reactions suitable for multiscale simulations of reaction– advection–diffusion systems”. In: Journal of Computational Science 63 (Sept. 2022), p. 101770. ISSN: 18777503. DOI:10.1016/j.jocs.2022.101770. [84] Vladimir Lobaskin and Roland R. Netz. “Diffusive-convective transition in the non-equilibrium charging of an electric double layer”. In: EPL (Europhysics Letters) 116.5 (Dec. 2016), p. 58001. ISSN: 1286-4854. DOI: 10.1209/0295-5075/116/58001. [85] Damien Toquer, Lydéric Bocquet, and Paul Robin. “Ionic association and Wien effect in 2D confined electrolytes”. In: The Journal of Chemical Physics 162.6 (Feb. 2025). ISSN: 1089-7690. DOI:10.1063/5.0241949. [86] Nikita Kavokine, Marie-Laure Bocquet, and Lydéric Bocquet. “Fluctuation-induced quantum friction in nanoscale water flows”. In: Nature 602.7895 (Feb. 2022), pp. 84–90. ISSN: 1476-4687. DOI:10 .1038 /s41586 -021 - 04284-7. [87] Damien Toquer, Baptiste Coquinot, Nikita Kavokine, and Lydéric Bocquet. In preparation. 20