This talk will discuss three distinct problems in engineering superconducting and magnetic material platforms, focusing on how interfaces and growth processes give rise to unconventional transport phenomena. I will present our studies on Josephson junction using hybrid weak link hetero-structures, and superconductivity in Ni/Bi hybrid structures. While both Ni and Bi have been extensively investigated individually, their interface exhibits an intriguing transverse transport response that cannot be understood within conventional expectations of longitudinal resistance measurements. I will discuss why an anomalous transverse response can emerge in such systems, the possible role of interfacial spin-orbit coupling and magnetic proximity effects, and what these observations imply for understanding electronic transport in magnetic–superconducting heterostructures. Next, I will focus on large-area thin films of the high-temperature superconductor BSCCO. I will present the synthesis strategy and growth optimization required to obtain high-quality films, highlighting the challenges associated with phase purity, stoichiometry control, and crystallinity. Finally, I will discuss the transport characteristics of these films and their implications for scalable superconducting devices and quantum transport platforms. Together, these problems illustrate how interfaces, growth conditions, and emergent transport properties play a central role in designing next-generation superconducting and magnetic materials systems.

Prof. Peter Hirschfeld from University of Florida will give a general talk addressed to an broad audience not specialized in suerconductivity research. The lecture provides a comprehensive qualitative introduction, including a historically oriented discussion of concepts in superconductivity theory. The second part of the lecture focuses on the recent research activities, specifically, on pressurized hydrides and how they inspired the Hirschfeld group to use AI methods to search for new electron-phonon superconductors.

The advancement of available AI models allows to tackle different challenges in Materials Science and Engineering. In this talk, applications of different AI models shall be discussed in the context of micromechanics. This allows to accelerate scientific workflows and automate tasks. The following examples will be discussed: (1) materials selection via simple models, (2) classification and segmentation for microstructure analysis as well as damage identification and quantification via deep learning, (3) fatigue lifetime prediction via graph neural networks and explainable AI approaches based on structured data from high-throughput small scale testing, (4) data and information extraction from publications via Large Language Models and (5) semantic extraction and ontology learning for MSE via agentic AI.
While AI accelerates and enriches our understanding of materials, reliability and uncertainties need to be critically reviewed. Therefore, training data, training methodologies as well as model selection are all relevant ingrediencies and will be discussed.
The talk will conclude by introducing national and international initiatives: NFDI, NFDI-MatWerk (since 2021), MaterialDigital (since 2019), and Materials Commons (2026 - 2030 EU) and how to participate.