Soft Matter Theory Group

The Soft Matter Theory Group, headed by professor Filip Uhlík, is a part of the Department of Physical and Macromolecular Chemistry of the Charles University in Prague. Our interdisciplinary research very much sits at the interface between physics and chemistry of macromolecules and biomacromolecules. It includes study of solutions of both synthetic and biological macromolecules; their thermodynamic and kinetic properties, such as self assembly, aggregation, diffusion, etc.

If you are interested in joining our group, as a graduate or undergraduate student, or as a postdoctoral fellow, please see the student topics section.

  • Boltzmann Inversion 
  • Molecular Dynamics
  • Monte-Carlo Methods
  • AI and Machine Learning in Chemistry
  • Quantum Chemistry
  • Mean Field Modelling
  • AI for Quantum computers simulation
  • AI learned potentials for coarse grained protein models
  • Hydrogel electrolyte batteries simulation
  • Nanoparticle diffusion in crowded media
  • Polyelectrolyte coacervation
  • Self healing (physical) hydrogels

People of the Group

Filip Uhlík

Prof. RNDr., Ph.D.

Associate professor

Lucie Nová

Ing., Ph.D.

Assistant professor

Oleg Rud

Ph.D.

Researcher

Peter Illés

Bc.

Master student

Matěj Lang

Bc.

Master student

Iryna Morozova

Bachelor student

Adam Zahradník

Bachelor student

Juraj Tkáč

Bachelor student

Research Topics

Simulations of polyelectrolytes

We study the behavior of polyelectrolyte solutions and gels, including linear, branched, star-like, and comb-like architectures. Our focus includes infinite gel networks, dissociation in weak polyelectrolytes, and polyampholytes, providing insights ...

Metallo-polyelectrolyte complexes

Metallo-polyelectrolyte complexes (MPECs) are physical gels formed by the interaction of long polyelectrolyte chains with multivalent metal ions (e.g., Ca2+, Al3+). These gels exhibit self-healing properties, making ...

AI for quantum computer

Fullerenes can encapsulate atoms as qubits for quantum computing. AI-trained potentials, based on ab initio data, enable efficient and accurate long-term simulations of these systems, advancing quantum technology.

Coarse-grained protein models

We use coarse-grained (CG) models to study protein dynamics efficiently. By applying Boltzmann inversion, we derive interaction potentials from atomistic data, preserving key structural features. These models help explore protein ...

Mean field modelling of polymers

We study hydrogels, coacervates, nanoparticles, and polymer brushes using large-timescale simulations. By combining Scheutjens-Fleer Self-Consistent Field Theory and hybrid SCF-Monte Carlo modeling, we explore their structure, dynamics, and material properties ...

Quantum chemistry

Our research employs quantum chemistry and Quantum Monte Carlo (QMC) simulations to investigate electronic structures and properties of molecular systems with high accuracy. QMC methods enable us to explore complex ...

Selected Publications