(4bm) Soft and Living Deformable Matter: Biophysical Insight and Bioinspiration from Geometry-Adapted Simulations

Ever since van Leeuwenhoek discovered “very little animalcules” in a drop of water in the 17th century, the diversity of cellular organisms has been recognized. Yet it took until the early 1900s with D`Arcy Thompson’s book On Growth and Form that it was fully established that geometry informs microscopic single cells and all other biological materials and systems. Nowadays we also know that the collective and self-emergent dynamics of active swarms in biology, colloidal science and modern-day robotics can be traced back mostly to the local interactions between neighbouring particles. While nature and robotics scientists developed intricate strategies/algorithms for organisms/robots to communicate on the local scale and thereby coordinate their global dynamics, we still lack the ability to synthesize colloidal particles with a similar degree of complexity. In that regard, I study computationally how shape and other morphological properties of active particles and their environment influence both the local structures and global dynamics in crowded active particle systems. In particular, I study the particle shape and asphericity dependence on active matter systems such as active Brownian particles and, more recently, flexicles (a new computational model for deformable self-propelled vesicular particles). Secondly, I am interested in how active matter interacts with complex environments and how their collective behavior can be controlled and predicted by the geometry of the confinements.

Research Interests

With my gained expertise in numerical tools (MC and MD simulations) and code development (HOOMD-Blue) as well as my background in differential geometry, topology and classical density theory, I am equipped to continue bridging the gaps between and study the physics behind processes in biology, chemical engineering and soft matter from the perspective of geometry. In my future research group, I will tackle especially an often-missing aspect of biology in colloidal systems and study the importance of morphological and geometric adaptability in the physics and functionality of multiparticle systems. For this task, I will ground my group's research on three pillars:

1. Smart cellular materials
The first part of my research involves the computational modelling and designing of deformable soft matter systems. For instance, I will develop strategies for bio-inspired autonomous, self-driven, artificial cellular robots that can mimic microbial sensing, locomotion and function skills. Furthermore, I will use machine learning and other mathematical modelling tools to find minimal sets of interparticle communication and sensing rules that allow for the creation of adaptive and responsive synthetic cellular tissues.

2. Geometry-induced assembly-disassembly cycles
The second pillar entails the theoretical description of active matter, especially concerning the physics of disassembly. By introduing geometrical frustration either externally or internally into colloidal crystal structures, this effort will unravel and copy nature’s strategies for fast structural reconfiguration and reveal design strategies for active recyclable matter based on geometrically induced disassembly.

3. Development of computational tools
Lastly, my group will focus on developing new simulation techniques and geometrical order parameters to study deformable materials. As supercomputers and computational techniques become increasingly powerful, we are getting closer and closer to modelling even the most complex biophysical processes. With my computational expertise, I not only want to be a part of but also drive this development.

Teaching Interests

The field of chemical engineering and physics has always experienced rapid evolution. Both soft matter physics and material science, which are among others driven by breakthroughs in quantum materials, advancements in experimental and computational techniques, and an increasing blending of soft matter and biophysics, are no exception. As the field continues to expand, it is more important than ever to prepare the next generation of engineers to approach scientific problems by teaching the fundamental concepts of statistical physics and thermodynamics while simultaneously integrating these advances in the classroom. Being a computational physicist I feel exceptionally capable of conceptualizing courses that rely on numerical methods and expand the portfolio of a department’s curriculum. Examples are classes in computer physics or numerical methods in biophysics, bioengineering and soft matter. However, I am also excited to teach fundamental classes such as condensed matter or statistical physics.