Research

Selected Research Projects

Our research interests are firmly in the soft matter space, with a focus on polymer solutions, melts, bio-inspired hybrid lipid systems, as well as colloidal self-assembly. We are investigating their behavior in and out of equilibrium. The following research projects are examples, although the list is aways changing based on exciting new topics, opportunities, and collaborations.

Hybrid Lipid-Polymer Membranes

Hybrid phospholipid block-copolymer membranes exhibit unique properties due to the synergy between robust polymer domains and biologically active lipid domains, or the presence of interfaces. Using coarse-grained molecular dynamics, we identify four morphologies—mixing, lateral phase separation, unzipping, and polymer-rich membranes—governed by hydrophobic mismatch and polymer concentration. Focusing on mixing and phase separation, we simulate various lipid and polymer combinations, showing how polymer block lengths and lipid bilayer features influence morphology. We also aim to determine the phase separation dynamics and explore strategies to stabilize finite-sized domains, enhancing the design of synthetic membrane systems.

Related publications:

  1. Lipid Membrane Leaflets Unzip upon Hybridization with Polymer-Rich Nanodomains
    JF Tallman, N Kambar, C Leal, A Statt, Macromolecules 57 (24), 11688-11696, 2024
  2. Simulating Curved Lipid Membranes Using Anchored Frozen Patches
    JF Tallman, A Statt, The Journal of Physical Chemistry B, 2025

Self Assembly of Sequence-defined Macromolecules

We are interested in how macromolecules (e.g. synthetic polymers, proteins, peptides,…) with defined sequences of different monomers (e.g., hydrophobic/hydrophilic, amino-acids,..) self-assemble in dilute solutions and which specific sequence motifs lead to what large-scale self-assembled structure. For this, we use a combination of MD simulations and ML techniques.

Related publications:

  1. Model for disordered proteins with strongly sequence-dependent liquid phase behavior, Statt, Casademunt, Brangwynne, and Panagiotopoulos, JCP, 152,  (2020)
  2. Unsupervised learning of sequence-specific aggregation behavior for a model copolymer, Statt, Kleeblatt, and Reinhart, Soft matter, 17, 33 (2021)
  3. Opportunities and challenges for inverse design of nanostructures with sequence defined macromolecules, Reinhart and Statt, Accounts of Materials Research, 2, 9 (2021)
  4. Predicting aggregate morphology of sequence-defined macromolecules with recurrent neural networks, Bhattacharya, Kleeblatt, Statt, and Reinhart, Soft matter, 18, 27 (2022)
  5. Predicting self-assembly of sequence-controlled copolymers with stochastic sequence variation, KA Curtis, A Statt, WF Reinhart, Soft matter 21 (11), 2143-2151 (2025)

Polymer Network Topology and Heterogeneity

Polymer networks are widely used, but the impact of spatial and topological heterogeneity on their structure and mechanical properties is not well understood. Using simple simulation models, we generate cross-linked polymer networks with controlled defects and investigate the effect of spatial heterogeneity (which reduces primary loops, contrary to trends in homogeneous systems). Our Molecular dynamics simulations can reveal that although heterogeneous networks have more elastically active strands, they break earlier and sustain lower stresses. With our models, we are in the position to  investigate how defects significantly alter network behavior, informing the design of advanced materials.

Related Publications:

  1. Interplay of spatial and topological defects in polymer networks, BR Argun, A Statt, ACS Engineering Au 4 (3), 351-358, 2024

Stress Responsive Polymer Materials

The development and design of highly specific responsive materials has great potential in many applications, ranging from self-healing or self-strengthening materials, shape recovering materials, chemical sensing or damage sensing, and bio-related control and release systems. Our group explores the fundamental questions of how macroscopic forces are transmitted to the micro-scale in heterogeneous, complex environments. We will use the generated fundamental knowledge to help design novel responsive materials.

Related publications:

  1. Computational study of mechanochemical activation in nanostructured triblock copolymers, Z Huo, SJ Skala, LR Falck, JE Laaser, A Statt
    ACS Polymers Au 2 (6), 467-477 (2022)
  2. Effect of polymer composition and morphology on mechanochemical activation in nanostructured triblock copolymers, Z Huo, S Arora, VA Kong, BJ Myrga, A Statt, JE Laaser, Macromolecules 56 (5), 1845-1854 (2023)
  3. Preferential Mechanochemical Activation of Short Chains in Bidisperse Triblock Elastomers, Z Huo, KF Watkins, BC Jeong, A Statt, JE Laaser, ACS Macro Letters 12 (9), 1213-1217 (2023)

Anisotropic Interactions of Non-spherical Colloids

Modern synthesis has made it possible to make diverse nanoparticles and colloids with various surfaces, shapes and sizes. Simulating interactions between non-spherical colloidal particles is computationally challenging due to the complex dependency of forces and energies on their geometry. We introduce and evaluate descriptor-based methods to make simulations more efficient. We accurately reproduced structural properties across diverse particle shapes including cubes, tetrahedra, pentagonal bipyramids, and twisted cylinders. Additionally, we are also interested in multi-face shapes with different interactions on their surfaces and shapes with no symmetries like twisted cylinders. Our simulations of complex colloidal systems and can potentially help to facilitate efficient studies on shape dependent interactions and phase behavior in the future.

Related publications:

  1. Influence of Shape on Heteroaggregation of Model Microplastics: A Simulation Study
    BR Argun, A Statt, Soft Matter 19 (43), 8081-8090, 2024
  2. Molecular Dynamics Simulations of Anisotropic Particles Accelerated by Neural-Net Predicted Interactions, B Rusen Argun, Y Fu, A Statt, Journal of Chemical Physics, 2024
  3. Machine-Learning Potentials for Efficient Simulations of Anisotropic Colloids
    BR Argun, A Statt, arXiv preprint arXiv:2509.15504, 2025