Öffentliche Seminare

Field theoretic simulations of block copolymers at realistic molecular weights

Block copolymers are known for their elaborate microphase separating capabilities. Due to the many tuning parameters a predictive modelling approach is required. Molecular dynamics for such large system sizes is expensive. On the other hand, self-consistent field theory is capable of simulating much larger systems, but its mean-field based approximation only becomes correct when the chain density becomes unrealistically high. As a result some experimental effects such as a first order order-to-disorder phase transition are not reproduced. Field-theoretic simulations, where fields are not constrained to their mean field value but allowed to fluctuate, bridge the gap between these methods. I will give an introduction into this method, with some of the associated phenomena such as the ultraviolet divergence, and successful applications in symmetric block copolymers and block copolymer-homopolymer blends. [mehr]

Methods and applications in integrative structural biology

Accurate structural models of biological systems can be obtained by integrative approaches that properly combine multiple sources of information, such as experimental data and a priori physico-chemical knowledge. In this talk, I will give an overview of the methodological approaches that we have been developing as well as a series of applications to systems of outstanding biological importance. Specifically, I will focus on the determination of structural ensembles of intrinsically disordered systems using NMR data and on the use of cryo-electron microscopy data to unravel the continuous dynamics of flexible parts of ordered systems. Finally, I will present an open-source, freely-available module of the PLUMED library (www.plumed.org), which enables the simultaneous determination of structure and dynamics of conformationally heterogeneous systems by integrating experimental data with a priori information. [mehr]
Innovative solar system missions must become increasingly innovative and elaborate since "the low-hanging fruits have already been picked." Solar sails, which are propelled solely by solar radiation pressure, are among the key technologies for the future exploration of the solar system because they make missions possible that would otherwise be infeasible due to their immense propellant requirements. The optimization of solar sail trajectories, however, is a difficult task. In the talk, a method is presented that is based on machine learning, fusing artificial neural networks and evolutionary algorithms. Such optimization methods may also be applied for subsurface ice melting probes, as they are required to explore Jupiter's and Saturn's icy moons, which may harbor life in the oceans beneath their thick ice crusts. Such ice melting probes have been developed at FH Aachen and successfully tested in Antarctic ice. It will be interesting to discuss whether those methods can also be applied in polymer research. [mehr]

Assembly, Cooperativity, and Emergence: From the AI-Guided Formation of Materials to the Onset of Soft Matter Robotics

Self-organization and assembly processes are crucial steps in the making of a wide range of materials and, in turn, have a great impact on their performance. For instance, the crystal structure, or polymorph, that forms during nucleation often dictates the bioavailability of pharmaceutical drugs, or the mechanical and catalytic properties of metal alloys and inorganic nanoparticles. In biology and medicine, protein folding and aggregation processes play a major role in the onset of many neurodegenerative disorders. Similarly, active, self-propelled, objects can form unexpected structures such as colloidal rotors on the micron scale, or bacterial biofilms, bird flocks and swarms of unmanned aerial systems on the macroscopic scale. While recent advances in experimental, theoretical & computational methods have allowed for unprecedented insights into the behavior of nonequilibrium systems, a complete understanding of these processes has remained elusive so far. For example, it is still impossible to predict which crystal structure forms when a liquid crystallizes. Similarly, the elucidation of the rules of life of swarms and active assemblies remains an outstanding challenge, although it is a necessary starting point to the successful development of soft matter robotics. In this talk, I discuss how my research group leverages computational materials science and artificial intelligence to shed light on assembly, cooperativity, and emergence in hard, soft and active matter. I show how recent advances in statistical mechanics and ML-guided simulations shed light on assembly pathways in materials and biological systems. I finally highlight how data science and machine learning methods provide a new way to accelerate discovery in soft autonomous robotics technology. [mehr]
I will present techniques to find reaction coordinates to be used in conjunction with free energy biasing techniques such as the adaptive biasing force method. This allows for instance to improve the sampling of configurations of complex proteins. However, reaction coordinates are often based on an intuitive understanding of the system, and one would like to complement this intuition or even replace it with automated tools. One appealing tool is autoencoders, for which the bottleneck layer provides a low dimensional representation of high dimensional atomistic systems. I will discuss some mathematical foundations of this method, and present illustrative applications including alanine dipeptide. Some on-going extensions to more demanding systems, namely HSP90, will also be mentioned by Zineb Belkacemi, the PhD student working on this project. [mehr]

Dynamic Load Balancing for Parallel Particle Simulations

Parallel computing has developed as a central tool in scientific computing to solve large scale problems involving huge number of degrees of freedom, complex geometries or coupled applications. The parallel efficiency is key for estimating to which degree the computational resources are used, or whether there is still potential to speed up an application by organising data or workflow in a different way across processors. To reduce the wall clock time of an application, a goal might be to use as many processors of a parallel architecture as possible. However, scalability of a parallel application depends on a number of characteristics, among which is efficient communication, equal distribution of work or efficient data layout.Many parallel applications, especially particle or mesh based algorithms like Molecular Dynamics or Lattice Boltzmann methods, are implemented by domain decomposition techniques, where processors administrate certain geometrical regions of a physical system. In such cases, unequal work load in the processor network is to be expected when particles are not distributed homogeneously or the computation cost of particle interactions is not equal in each part of the system. Also in the case where heterogeneous architecture components are coupled together in a complex cluster network (e.g. CPU-GPU, different types of CPUs or different network speeds) wall clock times for solving a problem with the same number of degrees of freedom will vary across the parallel application. For these scenarios the code has to decide how to redistribute the work among processes according to a work sharing protocol or to dynamically adjust computational domains, to balance the workload.In the seminar, I will give an introduction to the problem of load balancing and discuss various methods to redistribute data or re-organise the domain decomposition to improve and optimise the work load and to improve parallel efficiency and scalability. As an outlook I will discuss developments from the European Centre of Excellence E-CAM, where different methods have been implemented into a library, which can be used in community codes. [mehr]

Taming complex fluids with thermal fields

External fields, thermal and electromagnetic, induce a range of non-equilibrium effects in complex fluids consisting of nanoparticle suspensions (Soret, Seebeck, Peltier effects), which can be exploited in energy conversion (thermoelectrics), analytical devices for detection of biomolecules, or nanoparticle transport and assembly. The combination of Non-Equilibrium multiscale simulations and theory has paved the way to explain the physical behaviour of complex fluids under external fields, showing that their response is much richer than previously predicted. I will discuss how simulation techniques can be used to obtain thermophysical properties relevant in energy conversion problems and to uncover novel non-equilibrium effects in complex fluids, associated to the coupling of internal degrees of freedom of molecules and colloids with thermal fields. [mehr]

Dynamics of Soft Matter in Increasingly Complex Environments

Recent advancements in the understanding of the dynamics of soft matter is presented, concentrating on polymer melts with some outlook on semiconducting polymers and lipid membranes in solution. Traditionally, techniques like rheology are very popular, as these enable high-throughput experiments that help connect model materials with applications. Despite substantial progress, even the simplest model materials are not entirely understood, at least when it comes to a simultaneous modeling of experimental results from different techniques. This indicates that there is still a lack of information that prevents holistic understanding. This presentation concentrates on augmented analysis of recent experimental results on bottlebrush polymer melts, semiconducting polymers in solutions, and lipid membranes. Using the advantage of length- and time-scale dependent information of neutron spectroscopy, we distinguish different processes in polymer melts, nanocomposites, bottlebrushes, semiconducting polymers, and lipid membranes. As the results point to a generic picture, the procedures used appear to be a promising path to further elevate fundamental understanding of polymers, including confined chains and architectures of increasing complexity. [mehr]

Improving accuracy of systematic coarse-grained simulations

Billion atom simulations are just now becoming possible in molecular simulation for nanoseconds. We've also crossed the millisecond barrier for simulating biomacromolecules. What's left? Unfortunately, a typical cell contains 100 trillion atoms. Even simulating something like a polymer nanoparticle (~100 million atoms) has timescales of interest far beyond nanoseconds. One way around the length-scale limitation is coarse-grained simulation. Coarse-graining requires two ingredients: (i) a mapping that determines how to group atoms into coarse beads and (ii) a force field that describes these interactions. In this talk, I will describe our recent progress on determining mapping operators, which has previously been an arcane topic with little rigor. We've developed novel theory, shown what role symmetry plays, and developed ML models that find mappings for arbitrary molecular systems. Finding the force field of a coarse-grained model is a rich field with a long history. Typically, it is broken into two types: top-down, where we choose the force field to reproduce an observed phonemenon in experiment; and bottom-up, where we draw upon the observed forces in a molecular simulation. I will describe our recent work on combining these approaches to create hybrid top-down/bottom-up models via the principle of maximum entropy. [mehr]

Development of a coarse-grained model for liquid-like protein assemblies

In this talk, I will describe ongoing efforts in my group aimed at developing an accurate simulation model to study the thermodynamics and kinetics of multiprotein assembly. We use a "top-down" approach for constructing a Cα-based (one interaction site per amino acid) protein model. Development of the proposed model involves comparisons with experimental data available from the recent literature as well as comparisons with atomistic simulations of a single protein chain. The usefulness of our approach will be demonstrated by discussing results on multiple biological systems of interest. Of particular interest to us is the formation of liquid-like assemblies of disordered proteins that have been found to be important for the physiological function of membraneless compartments in living cells including the nucleolus and ribonucleoprotein (RNP) granules as well as many organelles in prokaryotic cells. [mehr]

Quantum computing and its applications in chemistry and physics

Quantum computing is emerging as a new paradigm for the solution of a wide class of problems that are not accessible by conventional high performance classical computers. Quantum computers can in principle efficiently solve problems that require exponential resources on classical hardware, even when using the best known classical algorithms. In the last few years, several interesting solutions with potential quantum speedup have been brought forward in the domain of quantum physics, like the quantum phase estimation and the hybrid variational quantum eigensolver [1] for the solution of optimization problems. The original idea that a quantum computer can potentially solve many-body quantum mechanical problems more efficiently than classical computers is due toR. Feynman who proposed the use of quantum algorithms to investigate the fundamental properties of nature at the quantum scale. In particular, the solution of the electronic structure and statistical mechanics problems is a challenging computational task as the number of resources increases exponentially with the number of degrees of freedom. Thanks to the development of new quantum technologies witnessed over the last decades, we have now the possibility to address this class of problems with the help quantum computers. To achieve this goal, new quantum algorithms able to best exploit the potential quantum speedup of state-of-the-art noisy quantum hardware have also been developed [2,3]. In this talk, I will first introduce the basics of quantum computing using superconducting qubits, focusing on those aspects that are crucial for the implementation of quantum chemistry and physics algorithms. In the second part, I will highlight the potential advantages of the new generation of quantum algorithms for applications in electronic structure calculations for ground [4] and excited states [5], molecular dynamics [6], and statistical physics [7]. [mehr]

Understanding the friction of atomically thin layered materials

Friction is a ubiquitous phenomenon that greatly affects our everyday lives and is responsible for large amounts of energy loss in industrialised societies. Layered materials such as graphene have interesting frictional properties and are often used as (additives to) lubricants to reduce friction and protect against wear. Experimental Atomic Force Microscopy studies and detailed simulations have shown a number of intriguing effects such as friction strengthening and dependence of friction on the number of layers covering a surface. Here, we propose a simple, fundamental, model for friction on thin sheets. We use our model to explain a variety of seemingly contradictory experimental as well as numerical results. This model can serve as a basis for understanding friction on thin sheets, and opens up new possibilities for ultimately controlling their friction and wear protection. [mehr]
Amyloid fibrils are well-ordered supramolecular polymers consisting of thousands of protein molecules connected via intermolecular hydrogen bonds. For intrinsically disordered proteins (IDP), amyloid form is thermodynamically more stable than the native form, and its formation in human body can lead to pathology. Namely, misfolding of small intrinsically disordered neuronal protein α-synuiclein is a hallmark of Parkinson's disease. The fibrillization is an autocatalytic process that can be induced by small amounts of pathological fibrils in a prion-like manner. We studied detailed kinetic mechanism of the α-synuiclein fibrillization and have shown that atypical sigmoidal reaction kinetics and exponential distribution of the length of formed fibrils are the results of a two-step autocatalytic cycle that includes fibril elongation via binding monomers to the ends and formation of new fibril ends due to fibril breaking [1]. This allowed us to identify the fibril ends as the bottleneck of the process and thus the most prospective target for fibrillization inhibitors. We designed several proteins and peptides that selectively bind to the fibril ends and block their growth by creating a steric hindrance [2,3]. This approach permits inhibition of fibril formation at inhibitor concentrations orders of magnitude lower than the concentration of monomeric α-synuclein. In my talk, I will focus mostly on the application of mathematical models for determination of the reaction mechanism based on kinetic data and on design of experiments for refining the models and proving the mechanism. [mehr]

Conducting polymers and bioinspired materials for organic bioelectronics

Organic electrochemical transistors (OECTs) have rapidly surged as amplifying transducers forbiosensing or diagnostic devices and for cells/nerves stimulation. 1 OECTs translate ionic signalsinto electric current using an electrolyte in direct contact with a conducting polymer channel.Unlike in organic field effect transistors where charge transport involves only a small layer, ionsin OECTs permeate the entire volume of the active material. This results in devices having highersignal amplification and lower operating voltage. 1 At the heart of OECTs working principle is theorganic mixed ionic-electronic conductor, usually a π- conjugated polymer (or polymer blend)able to host (or chemically linked to) charged groups.The greatest challenge of developing high-performing mixed conductors is optimizing theseemingly conflicting processes of electronic and ionic charge transport. 2 The chemical featuresof mixed conductors make them hydrophilic; however, little is known about how water and ionsaffect their microstructure and charge transport, as well as their long-term operational stabilityand biocompatibility.A successful strategy towards suitable materials for OECTs is to modify conducting polymerssuch as polythiophenes to make them hydrophilic, using glycolated substituents. 3 I am currentlyinvestigating the role of glycolated side chains in ion coordination and polymer morphology, 4,5and developing a methodology to describe swelling and morphology changes upon ionpenetration and doping in these materials.A different path towards new bioelectronic materials is to instead use natural mixed conductingpolymers that are intrinsically biocompatible, either alone or in composites. Synthetic polymersderiving from eumelanin (the black pigment in our skin, hair and eyes) are promisingbiocompatible, non-cytotoxic components in OECTs or other optoelectronic devices: 6-8 theyfeature both electronic and protonic charge carriers, can be prepared in large batches undercontrolled conditions, and easily incorporated into hybrid materials.I am currently studying the structure, self-assembly and electronic properties of eumelanin-derived materials. My model takes into account the chemical disorder of eumelanin(tautomerisation, oxidation and proton exchange sites) and aims to elucidate its effect on theelectronic structure and charge transport characteristics of this material. In particular, I aminvestigating the peculiar dipole-dependent properties of DHICA melanin 9 and itssupramolecular organisation; this rigid polymer can be spun into fibers with promisingmechanical and charge transport properties. [mehr]
The invention of new materials combined with an improved knowledge of structure-property relationships of organic donor-acceptor blends led to an impressive improvement in their energy conversion efficiency to 17 % [1]. This success seems to contradict the simple view that the long range Coulomb interaction between electrons and holes in organic semiconductors causes inefficient formation but efficient recombination of free charge. An important characteristic of organic solar cells is that they comprise at least two organic components of different chemical structure, introducing a large complexity of the morphology and electron landscape of the active layer. In this talk, I will present results regarding the generation and recombination of free charges in selected bulk heterojunction solar cells, with particular focus on the role of the interfacial CT state. We show that free charge formation proceeds predominately through low energy CT states, ruling out the predominance of a hot CT dissociation pathway, and that the same states dominate the subsequent recombination [2]. For fullerene-based solar cells with a low donor content, we find that the efficiency of charge generation is limited by the same mechanism that limits the VOC, namely non-radiative recombination of the CT states via vibronic coupling [3]. Notably, the rate of this recombination process obeys the classical energy gap law, implying that donor-acceptor blends benefit from a higher CT energy through longer CT lifetimes and more efficient photocurrent generation [4]. Consistent with this result, we observe that devices suffer from inefficient CT dissociation also through a higher rate of non-geminate recombination [5]. As a consequence, it’s only the systems with very efficient charge generation and very fast CT dissociation that the free carrier recombination is strongly suppressed, irrespective of the details of the spin statistics. We, finally, present recent results on a highly efficient polymer:NFA blend, where we find a surprisingly low activation energy for free charge generation, despite a low energy offset at the heterojunction [6]. These results highlight the importance of a comprehensive understanding of the energy landscape, and how it affects the pathway from the bound CT exciton to the spatially separated electron-hole pair. [mehr]
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