B01 – Toward intelligent light-propelled nano- and microsystems | Wittkowski
Julian Jeggle

In this project we work on developing theoretical models for suspensions of refractive light-driven particles with a symmetry breaking in shape and/or refractive index profile. While models such as Active Brownian Particles (ABP) only consider the close-range repulsive interaction between particles, refractive particles can also be subject to long-range interactions mediated by the light field as well as feedback effects created by external control of the light field via optical means. This makes them a viable building block for adaptive matter.
When it comes to theoretical descriptions of classical particle systems, there are two general approaches:

• Microscopic models, that describe the dynamics of each individual particle. Examples of this are Langevin dynamics¹, Brownian dynamics¹ or Ornstein-Uhlenbeck particles²
• Field theories, that describe the dynamics of order parameter fields like density and polarization. Example of this are (Active) Phase Field Crystal (PFC) models³, Dynamical Density Functional Theory (DDFT) models⁴, (Active) Model B⁵ or (Active) Model H⁶

While microscopic models are typically easier for formulate based on experimental observations of individual particles, they give less insight into collective phenomena like phase transitions emerging in many-particle systems. Field theories on the other hand are harder to formulate, but give a much better theoretical grip on collective effects.

Methods:

Besides the analytical methods connected to the models mentioned above we also make heavy use of numerical methods. At the level of particle dynamics this includes molecular dynamics simulations of many-particle systems as well as ray optic simulations to determine the interaction between refractive particles and a light field. At the level of field theories this mainly includes solving of partial differential equations by various means such as finite-difference or finite-element methods.
The numerical methods described above can require very large amounts of computational resources. To increase efficiency and make simulations of larger systems computationally feasible we are also invested in modern approaches to high-performance computing like offloading computations to GPUs, novel approaches to safe parallelism (Rust) or JIT-compilation via domain specific languages.

References

_{1. T. Schlick, Molecular Modeling and Simulation (Springer New York, 2002)}

_{2. L. L. Bonilla, Physical Review E 100 (2019)}

_{3. M. te Vrugt, J. Jeggle, R. Wittkowski, New Journal of Physics 23, 063023 (2021)}

_{4. M. te Vrugt, H. Löwen, R. Wittkowski, Advances in Physics 69, 121–247 (2020)}

_{5. R. Wittkowski et al., Nature Communications 5 (2014)}

_{6. A. Tiribocchi, R. Wittkowski, D. Marenduzzo, M. E. Cates, Physical Review Letters 115 (2015)}

B01 - Propulsion of Light-Responsive Nano- and Microsystems | Denz
Matthias Rüschenbaum

In our project, we design and investigate artificial light-propelled micro-swimmers. The propulsion of these particles is based on refraction of light with a net force resulting from asymmetric particle shapes and symmetry-broken refractive index profiles. Our task is the experimental realization of these particles and their subsequent light-controlled propulsion. For fabrication, two-photon polymerization (TPP) in a direct laser writing (DLW) setup is employed. Together with numerical simulations of our B01 team partners, the geometries, refractive index profiles as well as the complexly structured light used are optimized for e.g. highest velocities, followed by investigations of a high number of these particles, thereby creating artificial colloidal swarming structures. We also envisage mixtures of particles with different features including light-activated shape-changes. The ultimate goal of this project is the realization of memory effects and intelligent behavior in dense solutions of light-propelled micro-swimmers through structured illumination-based delay and added feedback

 

B02 - Adaptive Polymer Morphologies Through Reversible Block Fragmentation | Gröschel
Yorick Post

In our contribution to CRC 1459, we will develop dissipative block copolymer nanostructures and explore their application as sensors, actuators, and memory in synthetic adaptive and intelligent systems.  Block copolymers consist of two or more covalently linked incompatible segments capable of self-assembling into complex nanostructures, such as micelles, vesicles & cubosomes. Driven by differences in chemical affinity, like solubility, the resulting nanostructure is primarily defined by the lengths of the various blocks. We are designing block copolymers that can actively alter their composition through energy-driven block fragmentation. By incorporating and tuning orthogonal and multivalent fragmentation mechanisms the stability, lifetime and self-assembly behavior of the block copolymer aqueous solution can be adjusted. The resulting out-of-equilibrium nanostructures can only exist under a sufficient energy supply. These nanostructures will be stabilized into dynamic steady states by regulating the energy supply through the implementation of intrinsic feedback mechanisms.

B04 - Multistimuli sensing with memory and feedback function using photoswitchable proteins and coordination chemistry | Wegner
Alice Casadidio

We aim to create responsive matrixes such as hydrogels, which are sensitive to various inputs (from different colors of visible light to redox and pH). By combining photoswitchable proteins and coordination chemistry, we aim to achieve the formation of such materials, pursuing the development of memory due to multiple stimulation. Furthermore, we aim to investigate whether is possible to integrate signals in order to generate feedback. Together, these building blocks will give us access to processing molecular information through hydrogels.

B04 - Multistimuli sensing with memory and feedback function using photoswitchable proteins and coordination chemistry | Wegner
Saskia Frank

We will develop a hydrogel with multistimuli responsive crosslinks that can sense diverse input signals, process them following a chemically defined logic and respond with an output signal. Crosslinks will be mediated by photoswitchable proteins that convey responsiveness towards light of different wavelengths and metal coordination complexes which are sensitive towards changes in pH, redox potential and the presence of small soluble molecules. Some signals can also be integrated into the hydrogel matrix, thus creating a memory, in which I am particularly interested in my project.

 

B04 - Multistimuli sensing with memory and feedback function using photoswitchable proteins and coordination chemistry | Wegner
Bas Wennemar

We aim to create new photoswitchable proteins to realize non-invasive spatiotemporal control of cell-material interactions. These proteins can be used in a wide variety of applications in the field of biomaterials, biosensing, and fundamental cell biology studies.

B05 - Investigating Molecular Forces in Focal Adhesions, Hemidesmosomes and Adaptive Hydrogels | Grashoff
Theresa Mösser

The aim of our project is the generation of a conceptually novel, biosynthetic material in which mammalian cells are utilized as information-processing elements that sense, integrate, and feedback on mechanical stimuli. Together with the Strassert and Trappmann groups, we manufacture and characterize 3D hydrogels, in which mammalian cells bestow the hybrid material with a mechanical memory. This system is monitored by fluorescent biosensors that are purified and integrated into the cell-matrix hydrogel, so that mechanical signals can be quantified with fluorescence lifetime imaging. By gradually increasing the complexity of the hybrid material, responsive and adaptive features will be incorporated.

 

B05 - Role of Cellular Mechanotransduction in Cell Adhesion and Migration | Trappmann
Inka Schröter

The aim of our project is the generation of a conceptually novel, biosynthetic material in which mammalian cells are utilized as information-processing elements that sense, integrate, and feedback on mechanical stimuli. Together with the Strassert and Grasshoff groups, we manufacture and characterize 3D hydrogels, in which mammalian cells bestow the hybrid material with a mechanical memory. This system is monitored by fluorescent biosensors that are purified and integrated into the cell-matrix hydrogel, so that mechanical signals can be quantified with fluorescence lifetime imaging. By gradually increasing the complexity of the hybrid material, responsive and adaptive features will be incorporated.

C02 - Opto-electronic neuromorphic architectures | Pernice
Ivonne Bente

Photonic memory and computing devices are an emerging technology to be used for machine learning and artificial intelligence applications. We use chalcogenide phase-change materials (PCM) to implement memory functionality in the integrated photonic circuits. In our research, light from a waveguide couples evanescently to a PCM patch which influences the transmission through the waveguide depending on the phase state of the PCM. By decreasing the size of the PCM volume, the switching speed can be increased while the needed switching energy can be decreased. Additionally, we use plasmonic nanoantennas to enhance the inter­action between the electric field and the PCM. Therefore, we develop a method to fabricate nanometer-sized PCM cells embedded in the gap of gold dimer nanoantennas on a waveguide. These devices are promising candidates for the use in material learning, machine learning in materio, and pattern recognition applications. 

C03 - Self-Assembly of Hybrid Nanostructures for Neuromorphic Electronics | van der Wiel
Marc Beuel

Previous research¹ has shown that disordered networks of functionalized nanoparticles can be configured to behave like Boolean logic gates and classifiers. The functionalized nanoparticles act as single-electron transistors, i.e. strongly nonlinear periodic switches, while the organic ligands act as tunable tunnel barriers that add memory functionality to the network. The purpose of this research project is to build on these results and enhance the functionality by introducing various novel organic ligands and magnetic nanoparticles to enhance the addressability and to introduce memory to tackle new time-dependent problems and realize artificial neural networks that will pave the way for new applications of energy-efficient in-materio computing.

Reference:

_{1. Bose, S., Lawrence, C., Liu, Z. et al. Evolution of a designless nanoparticle network into reconfigurable Boolean logic. Nature Nanotech10, 1048–1052 (2015)}

C03 - Kinetic Monte Carlo Model for Computing Functionalities in Nanoparticle Networks | Heuer
Jonas Mensing

The theoretical underpinning of the experimentally studied nanoparticle networks is investigated by developing a physical model and subsequent simulations. For this purpose, a highly optimized parallel C++ code is being developed in order to model the charge transport within the electrically tunable network stochastically, i.e. with a Kinetic Monte Carlo approach. Requirements for computing functionalities such as Boolean logic and memory functionalities based on intelligent materials are examined. Therefore, statistical and data-driven tools are being developed to investigate the effects of different materials and system sizes. A close comparison with corresponding experiments on nanoparticle networks will be performed.

 

C04 - Spin Wave Systems for Reservoir Computing | Pernice
Dmitrii Raskhodchikov

Today, scientists are actively looking for a replacement for conventional electronics. One of the promising areas is spintronics - where information is transmitted not directly by electrons, but by means of spin. This approach allows operations to be completed faster and significantly improves the energy efficiency of devices. The propagation length of spin-waves in amorphous yttrium iron garnet (YIG) is sufficient for the use of this material in electronics and it combines well with existing materials both during operation and in production. The quanta of spin-waves are magnons: the dynamic eigen-excitations of a magnetically ordered body. Analogous to electric currents, magnon-based currents can be used to carry, transport and process information. The use of magnons allows the implementation of novel wave-based computing technologies free from the drawbacks inherent to modern electronics, such as dissipation of energy due to Ohmic losses. We will realize adaptive magnonic networks in a complex system comprised of a large number of coupled spin-waveguides, which transform the input of electrical data into spatiotemporal patterns in a high-dimensional space using nonlinear interference of spin-waves. 

C06 - Electro-Optical in-Memory Computing With Phase Change Materials | Salinga
Niklas Vollmar

Processing artificial neural networks requires powerful hardware. Today, they are usually calculated on conventional computers based on the von Neumann architecture. In this architecture, the processing unit is separated from the memory. Moving data from the memory to the processing unit, however, is costly in terms of time and energy. Hence, collocating processing and memory in so-called in-memory computing systems can make these calculations significantly more efficient. A novel and promising approach are systems based on integrated photonics. Here, optical signals are passed through waveguides and the weight of artificial synapses are represented by analog memory cells. I am working on mixed electro-optical memory cells based on phase-change materials. In such devices, a thin film of phase-change material is located on top of a photonic waveguide. The transmission through the waveguide can be tuned based on how much of the material is crystalline, and how much is amorphous. This can be probed with short laser pulses, taking advantage of the high data modulation rate and low latency of photonic systems. The state of the phase-change material can be changed by local heating with an electrical current. To optimize the performance of the electro-optical devices, I also study new materials with optimal properties. Utilizing both the electrical and optical domain in a single system is beneficial for learning, contributing to the development of intelligent matter.