Polymer materials in solution can show complex and sometimes unexpected behavior. Some of them form gel-like structures that respond to changes in temperature, pH, solvent type, or electric fields. In certain cases, they act as adaptive systems with memory-like responses. Remarkably, some polymer networks can even mimic neural networks by reorganizing themselves under external stimuli. These properties make polymer gels and related soft materials highly relevant for biomedical devices, sensors, soft robotics, and functional materials for extreme environments.
This project applies a multiscale modeling approach to understand and predict the behavior of such systems. Quantum mechanical (QM) methods, including density functional theory (DFT), are used to study non-covalent interactions like hydrogen bonding, dispersion forces, and electrostatics. Molecular dynamics (MD) simulations provide insights into the structure and mobility of polymer chains in solution, including gelation, aggregation, and phase transitions. Both gel and non-gel states are investigated.
Coarse-grained (CG) models, such as Dissipative Particle Dynamics (DPD), MARTINI, cellular automata (CA) are employed to simulate mesoscale processes like fibril formation, pore development, and solvent-induced restructuring. Finite element methods (FEM) connect simulation results to macroscopic properties such as elasticity, diffusion, and mechanical stability under real-world conditions.
Machine learning (ML) techniques are used to develop surrogate models that predict material properties based on chemical structure and synthesis parameters. This includes ML interatomic potentials trained on QM data, data-driven analysis of literature, and prediction of gel behavior across a wide design space.
Experimental data from small-angle X-ray and neutron scattering (SAXS, SANS), rheology, calorimetry (DSC), and microscopy are provided by collaborating laboratories. These data are essential for validating and improving the simulations.
The project aims to create a digital platform for the rational design of polymer gels and aerogels. A structured ontology and database will connect chemical composition, processing conditions, and material properties into an integrated knowledge system.
