Investigation of tracer diffusion in crowded cylindrical channel

Based on a coarse-grained model, we carry out molecular dynamics simulations to analyze the diffusion of a small tracer particle inside a cylindrical channel whose inner wall is covered with randomly grafted short polymeric chains. We observe an interesting transient subdiffusive behavior along the cylindrical axis at high attraction between the tracer and the chains, however, the long time diffusion is always normal. This process is found to be enhanced for the case that we immobilize the grafted chains, i.e. the sub-diffusive behavior sets in at an earlier time and spans over a longer time period before becoming diffusive. Even if the grafted chains are replaced with a frozen sea of repulsive, non-connected particles in the background, the transient subdiffusion is observed. The intermediate subdiffusive behavior only disappears when the grafted chains are replaced with a mobile background sea of mutually repulsive particles. Overall, the long time diffusion coefficient of the tracer along the cylinder axis decreases with the increase in system volume fraction, strength of attraction between the tracer and the background and also on freezing the background. We believe that the simple model presented here could be useful for a qualitative understanding of the process of macromolecular diffusion inside the nuclear pore complex

Physics informed Neural Networks applied to the description of wave-particle resonance in kinetic simulations of fusion plasmas

The Vlasov-Poisson system is employed in its reduced form version (1D1V) as a test bed for the applicability of Physics Informed Neural Network (PINN) to the wave-particle resonance. Two examples are explored: the Landau damping and the bump-on-tail instability. PINN is first tested as a compression method for the solution of the Vlasov-Poisson system and compared to the standard neural networks. Second, the application of PINN to solving the Vlasov-Poisson system is also presented with the special emphasis on the integral part, which motivates the implementation of a PINN variant, called Integrable PINN (I-PINN), based on the automatic-differentiation to solve the partial differential equation and on the automatic-integration to solve the integral equation

Renormalized charge and dielectric effects in colloidal interactions: a numerical solution of the nonlinear Poisson–Boltzmann equation for unknown boundary conditions

The Derjaguin–Landau–Verwey–Overbeek (DLVO) theory, introduced more than 70 years ago, is a hallmark of colloidal particle modeling. For highly charged particles in the dilute regime, it is often supplemented by Alexander’s prescription (Alexander et al. in J Chem Phys 80:5776, 1984) for using a renormalized charge. Here, we solve the problem of the interaction between two charged colloids at finite ionic strength, including dielectric mismatch effects, using an efficient numerical scheme to solve the nonlinear Poisson–Boltzmann (NPB) equation with unknown boundary conditions. Our results perfectly match the analytical predictions for the renormalized charge by Trizac and coworkers (Aubouy et al. in J Phys A 36:5835, 2003). Moreover, they allow us to reinterpret previous molecular dynamics (MD) simulation results by Kreer et al. (Phys Rev E 74:021401, 2006), rendering them now in agreement with the expected behavior. We furthermore find that the influence of polarization becomes important only when the Debye layers overlap significantly

In Situ Enhancement of Heliostat Calibration Using Differentiable Ray Tracing and Artificial Intelligence

The camera target method is the most commonly used calibration method for heliostats at solar tower power plants to minimize their sun tracking errors. In this method, individual heliostats are moved to a white surface and their deviation from the targeted position is measured. A regression is used to calculate errors in a geometry model from the tabular data obtained in this way. For modern aim point strategies, or simply heliostats in the rearmost end of the field, extremely high accuracies are needed, which can only be achieved by many degrees of freedom in the geometry model. The problem here is that the camera target method produces only a very small data set per heliostat, which limits the number of free variables and thus the accuracy. In this work, we extend existing ray tracing methods for solar towers with a differentiable description, allowing for the first time a data-driven optimization of object parameters within the ray tracing environment. Therefore, the heliostat calibration can take place directly within the ray tracing environment. Thus, the image data acquired during the measurement can be processed directly and more information about the orientation of the heliostat can be obtained. Within a simple example we show the advantages of the method, which converges faster and corrects errors that could not be considered before. Without any disadvantages or additional costs, the state-of-the-art calibration method can be improved

In-Situ Solar Tower Power Plant Optimization by Differentiable Raytracing

Solar tower power plants deliver climate-neutral electricity and process heat and can play a key role to facilitate the ongoing energy transition. These plants reflect sunlight with thousands of mirrors (heliostats) to a receiver and can generate temperatures over 1000°C. In practice, a plant must be operated with safety margins as even small surface deformations and heliostat misalignments can locally lead to dangerous temperature peaks. These imperfections are difficult to assess and limit the plant's efficiency, which hinders commercial success in a competitive market. We present a computational technique that predicts the incident power distribution of each heliostat including the inaccuracies based solely on focal spot images that are already acquired in most solar power plants. The method combines differentiable ray tracing with a smooth parametric description of the heliostat and reconstructs flawed mirror surfaces with sub-millimeter precision. Applied at the solar tower plant in Jülich, our approach outperforms all alternatives in accuracy and reliability. The approach can be integrated into the existing infrastructure and plant control at low cost, leading to increased efficiency of existing and decreased expenses for future power plants and supports establishing a new, green energy technology. For other fields, our approach can be a blueprint. We implement a common simulation technique in the Machine Learning framework PyTorch, leveraging automatic differentiation and GPU computation. By combining gradient-based optimization methods and a tunable parametric heliostat model, we overcome the high data requirements of data-centric methods while at the same time maintaining the flexibility required for modeling a complex real-world system

Efficient algorithms for electrostatic interactions including dielectric contrasts

Coarse grained models of soft matter are usually combined with implicit solvent models that take the electrostatic polarizability into account via a dielectric background. In biophysical or nanoscale simulations that include water, this constant can vary greatly within the system. Performing molecular dynamics or other simulations that need compute exact electrostatic interactions between charges in those systems is computationally demanding. We review here several algorithms developped by us that perform exactly this task. For planar dielectric surfaces in partial periodic boundary conditions, the arising image charges can be either treated with the MMM2D algorithm in a very efficient and accurate way, or with the ELC term that enables the user to use his favorite 3D periodic Coulomb solver . Arbitrarily shaped interfaces can be dealt with using induced surface charges with the ICC algorithm. Finally, the local electrostatics algorithm MEMD (Maxwell Equations Molecular Dynamics) allows even to employ a smoothly varying dielectric constant in the systems. We introduce the concepts of these three algorithms, and an extension for the inclusion of boundaries that are to be held fixed at constant potential (metal conditions). For each method, we present a showcase application to highlight the importance of dielectric interfaces

Providing AI expertise as an infrastructure in academia

Artificial intelligence (AI) is proliferating and developing faster than any domain scientist can adapt. To support the scientific enterprise in the Helmholtz association, a network of AI specialists has been set up to disseminate AI expertise as an infrastructure among domain scientists. As this effort exposes an evolutionary step in science organization in Germany, this article aspires to describe our setup, goals, and motivations. We comment on past experiences, current developments, and future ideas as we bring our expertise as an infrastructure closer to scientists across our organization. We hope that this offers a brief yet insightful view of our activities as well as inspiration for other science organizations