Leibniz MMS Days 2026
March 2 - March 4, 2026
Frankfurt (Oder)

Posters

A poster session will take place on March 2, 2026 in the afternoon.

Extending the model lid of UA-ICON

Tom Dörffel (IAP Kühlungsborn)

We present recent efforts on extending the upper model lid of the upper-atmosphere extension of the Icosahedral Non-hydrostatic modeling framework. This presentation will highlight the mathematical and numerical challenges and presents high-resolution results of global whole-atmosphere simulations from ground to 250 km.

Funding for Diamond-OA-Journals. With KOALA from passion to profession

Holger Israel (TIB Hannover)

We present recent efforts on extending the upper model lid of the upper-atmosphere extension of the Icosahedral Non-hydrostatic modeling framework. This presentation will highlight the mathematical and numerical challenges and presents high-resolution results of global whole-atmosphere simulations from ground to 250 km.

Integration of logical tensor networks into LLMs for explainable and efficient reasoning

Janina E. Schütte (WIAS Berlin)

Large language models (LLMs) have found their way into everyday life. However, their underlying mechanism, next-token prediction based on learned probability distributions, does not guarantee logical understanding. This leads to challenges in explainability and reasoning. In contrast, highly efficient logical tensor networks offer a powerful neuro-symbolic approach for representing relational and logical knowledge and for performing reasoning. Despite their advantages, the construction and manipulation of such networks often require domain expertise, limiting their accessibility to practitioners. To address these complementary limitations, we propose an agentic framework that integrates probabilistic LLMs with logical tensor networks, combining intuitive language-based interaction with efficient, logic-driven reasoning.

Machine Learning Applications in Low-Temperature Plasma Research Beyond Supervised Learning

Ihda Chaerony Siffa (INP Greifswald)

Machine learning (ML) has proven to be an indispensable set of tools in many industries and scientific fields, and low-temperature plasma (LTP) science and technology is not an exception. LTP research has enabled various technologies of high societal significance, and here, ML can be of use to improve the modeling and analysis of complex LTP systems. Applications of ML in LTP research have mostly focused on supervised learning, which depends on a large amount of labeled data. In this contribution, we bring forth examples of ML applications across various learning paradigms. First, we demonstrate a supervised learning approach to construct surrogate models for LTP simulations. Second, we introduce physics-informed neural networks (PINNs) utilized to solve complex physical equations in LTP physics, such as the electron Boltzmann equation, in a self-supervised manner. Lastly, we present a preliminary result of reinforcement learning for optimizing equivalent circuits of gas discharges based on electrical measurement data. These examples demonstrate various ML methods beyond the typical supervised learning approach.

Mathematical Modeling of Ethylene Dynamics in Fruit Storage Using Respiration-Based Physical Measurements

Akshay Dagadu Sonawane (ATB Potsdam)

Ethylene plays a central role in regulating fruit ripening, but accurate real-time measurement is limited due to the scarcity and cost of reliable sensors. To address this, a modeling approach was developed to predict ethylene production using fruit respiration rates and CO² dynamics. The model uses O², CO², relative humidity, and temperature as input parameters, providing a physics-based framework to estimate ethylene dynamics in storage and in small-scale sensor boxes without direct ethylene measurement. A small sensor box, acting as a respirometer, was placed inside the storage to capture fruit respiration by flushing storage air. Respiration rates were then correlated with ethylene production rate, and CO² diffusion was linked to ethylene diffusion, enabling the development of a predictive model using mass balance. This approach allows monitoring of ripening behavior and climacteric responses in stored fruit.

Modelling fracture-controlled geothermal reservoirs with application to fault-damage zones in the Upper-Rhine Graben, Germany

Ernesto Meneses Rioseco (Georg-August-Universität Göttingen, Geoscience Center)

The DEKAPALATIN-BERTHA joint research project aims to advance the use of deep geothermal energy from deep-seated reservoirs in the central Upper-Rhine Graben to support the decarbonization of the heating sector. Because the geological targets are fault damage zones, numerical modelling of thermo-hydraulic processes in fault- and fracture-controlled reservoirs is essential for ensuring sustainable and optimized operation of future geothermal doublets. This contribution presents a modelling framework that integrates fracture network geometry, fault-zone petrophysical properties, and coupled fluid heat transport to assess reservoir performance. Applied to representative fault damage zones in the Upper-Rhine Graben, the results highlight the importance of fracture connectivity and permeability contrasts for long-term thermal recovery and operational stability.

Pore-scale simulation of interphase mass transfer during two-phase flow in porous media.

Huhao Gao (LIAG Hannover)

Two-phase flow and interphase mass transfer in porous media are important processes for a wide range of scientific and engineering applications, such as geological storage of carbon dioxide and the remediation groundwater contaminants. This study builds a new interphase mass transfer model for the pore-scale direct numerical simulations. The model employs a continuous mass transfer formulation based on the phase field method. We verify the model with the analytical solutions of transport involving advection, reaction and diffusion processes. The model is tested for two-phase flow conditions in a conceptual 2D slit. The applicability of the model is demonstrated in NAPL/water drainage scenarios in a conceptual porous domain, comparing the results in terms of the spatial distribution of the phases and solute concentration.

Quantum applications: Role of strain-engineered Germanium (Ge)

Meera Bishnoi (IHP Frankfurt/Oder)

Germanium (Ge) is an intriguing material because of its high hole mobility, strong spin-orbit coupling, and near-direct bandgap, making it very attractive for next-generation quantum devices. This work explores strain engineering in Ge caused by silicon nitride (SiN) stressors, using finite element method (FEM) simulations in COMSOL to calculate normal and shear strain components resulting from different SiN stressor geometries, such as height, spacing between stressors, and stress levels. The resulting tensor is applied to a 6x6 Bir Pikus Hamiltonian to determine heavy-hole (HH) and light-hole (LH) splitting, which can be adjusted from 36 to 165 meV under 1-4 GPa of stress. The study emphasizes the significant effect of uniaxial and biaxial strains (including shear strain) on valence band ordering. This work shows that SiN stressors offer a precise, scalable, and CMOS-compatible method for tailoring Ge band structure in hole spin qubits and quantum wells.

Single-pixel imaging for Ge-based metasurface photodetectors

Paul Oleynik (IHP Frankfurt/Oder)

Structuring the absorbing layer of photodetectors as metasurfaces enhances device performance and enables functionalities such as wavelength- and polarization-selectivity for imaging applications. However, even if the fabrication of single pixels can be achieved, the fabrication of pixel arrays presents significant additional challenges, motivating other approaches such as single-pixel imaging. Single-pixel cameras can produce spatially-resolved images with single (or few) pixels by using a digital mirror device to spatially select optical data and direct it towards the photodetector. In conjunction with compressed sensing, this can be used to evaluate device performance at an early stage. Here, we present the current status of our single-pixel setup for multispectral imaging at visible and short-wave-infrared wavelengths. We present first results not only on image acquisition but also on utilizing metasurface-based photodetectors for wavelength-selective imaging.

Temperature-dependent performance of a GeSn thermophotovoltaic cell

Intatii Zaitsev (IHP Frankfurt/Oder)

Thermophotovoltaics (TPV) shows promise for direct heat-to-electricity conversion. Unlike solar cells that are powered by the Sun, TPV sources (concentrated sunlight heating, waste heat, power beaming), typically at 1000-2000 K, are closer, which yields radiation with significantly higher power densities (5-60 W/cm² versus ~0.1 W/cm² for solar) but below the bandgaps of many common semiconductors. This has drawn attention toward low-bandgap, CMOS-compatible materials (particularly group-IV semiconductors such as germanium (Ge) and germanium-tin alloys (GeSn). This work presents a comprehensive finite element model (FEM) of a TPV system employing a low-bandgap, direct-gap GeSn photovoltaic cell, accounting for indirect recombination, multilayer generation dynamics, and the thermal equilibrium between emitter and cell. It supports in-depth study of Ge-based materials under varying strain, composition, and temperature, providing insights for the design of future devices.