Beatriz Noheda (UG). RT-3: Materials synthesis and functionality.
Saleem Denholme. (COMMPHYS) TL-4: Academic publications and public dissemination.
What do publishers do? How can authors maximise the impact of their research? Academic publishers have been around for more than 200 years but the details of how a journal functions and the processes that guide a paper as it moves from submission to publication are often considered obscure by those working outside of scholarly publishing. In this presentation, I will discuss how the Nature Portfolio provides a platform for the dissemination of scientific research across a range of topics and how editorial teams work with authors, reviewers and the wider community in selecting, publishing and advancing scientific discovery.
Students' presentations
Dennis Meier (NTNU), Armin Satz (IFAT). TL-5: Scientific presentation
In this session, we will have an open discussion about how to present your scientific results to your colleagues and peers. The focus will be on scientific conference / workshop presentations, where you can expect an audience with certain knowledge about your research field, but also with very diverse experimental or theory backgrounds. You will self-evaluate your presentations given at the TOPOCOM meeting, receive some helpful feedback, and we will discuss some general basic guidelines that can help you to further improve your presentations.
Chirstof Melcher. Mathematical framework for topological solitons
This mini-course introduces key concepts from differential topology and explores how they arise naturally in variational problems of physics. Building on the calculus of differential forms, we will explore topological invariants and emphasize their roles in various field theories. The aim is to provide both mathematical insight and physical motivation, highlighting the deep interplay between topology, geometry, and analysis in understanding the emergence of vortices, skyrmions, and hopfions.
Natalya Fedorova (LIST). Introduction to ferroelectrics
In this lecture, we will explore the fundamental concepts of ferroelectrics, a class of materials that exhibit spontaneous electric polarization reversible by an external electric field. We begin by examining ferroelectric phase transitions within the framework of phenomenological Landau theory, initially focusing on bulk ferroelectrics with spatially uniform polarization. We then extend this approach to the Landau–Ginzburg theory, allowing the polarization to vary in space. Building on this foundation, we incorporate surface boundary conditions relevant for thin films, where screening mechanisms and depolarizing fields play a crucial role in stabilizing or destabilizing the ferroelectric state. We discuss the formation of domains and the emergence of complex polar structures—such as vortices, bubbles, and skyrmions—highlighting the rich variety of phenomena that can arise in these intriguing materials.
Valeria Bragaglia (IBM). Materials and Devices for Beyond von Neumann Computing
Brain inspired computing is a promising paradigm of artificial intelligence (AI) systems that aims at developing an efficient computing architecture that resembles the biological brains. The development of novel materials and devices with neural and synaptic functions incorporated into unique architectures will allow the implementation of a computing system that can efficiently perform the heavy vector - matrix manipulation inherent to AI workloads with O(1) time complexity[1]. Memristors are key building blocks for the realization of the artificial neural and synaptic function in neuromorphic computing. [2] These devices rely on diverse physical mechanisms and materials and their understanding via experimental and theoretical means is pivotal to the device optimization and coupling to the higher layers of the computer architecture. We will see examples of how material and device engineering can lead to breakthroughs in device performance. Nonetheless, device variability among other non-idealities still hinders the hardware scaling to large networks required to solve more complex AI tasks. Challenges at device and hardware level may also be overcome through a complementary research effort to develop more robust, hardware-friendly algorithms and computational models that could compensate for the variability issues of devices[3]. [1] A. Sebastian et al., "Memory devices and applications for in-memory computing", Nature Nanotechnology, 15, 529-544, 2020. [2] From: https://spectrum.ieee.org/analog-ai, 2021 [3] N. Gong, et al., “Deep learning acceleration in 14nm CMOS compatible ReRAM array: device, material and algorithm co-optimization” IEEE IEDM 33.7. 1-33.7. 4, 2022
Mehmet Onbasli (Koç Univercity). Interface-Induced Stability of Nontrivial Topological Spin Textures: Unveiling Room-Temperature Hopfions and Skyrmions
Topological spin textures, such as skyrmions and hopfions, are emerging as promising candidates for next-generation information storage and processing due to their stability, topological protection, and low energy dissipation. However, realizing these textures at room temperature and without external magnetic fields has remained a significant challenge. In this talk, I will present our recent discovery of robust, zero-field skyrmion–hopfion assemblies stabilized at ambient conditions in EuS–Bi₂Se₃–EuS trilayer heterostructures. By leveraging interfacial Dzyaloshinskii–Moriya interactions (DMI) and geometrical confinement, we achieve spontaneous formation of skyrmion lattices encircled by hopfion rings. These textures were directly visualized using Lorentz transmission electron microscopy and confirmed via depth-sensitive polarized neutron reflectometry and SQUID magnetometry. Micromagnetic simulations based on the Landau–Lifshitz–Gilbert framework further reveal the critical roles of uniaxial anisotropy, DMI strength, and interfacial coupling in stabilizing these multidimensional textures. Our findings demonstrate that topological insulator-ferromagnet interfaces provide a fertile platform for engineering robust chiral spin structures at room temperature, paving the way for energy-efficient spintronic devices and potentially interfacing with quantum states for topological quantum computing applications.
Stavros Komineas (UOC). RT-4: Topology and unifying concepts.
Magnets are described by the Heisenberg model which gives the Landau-Lifshitz equation in the case of ferromagents. This is a conservative model but can be extended to include damping, sourses, and other phenomena. A comparison with models in ferroelectrics will be given.
George Arampatzis (University of Crete). A Brief Introduction to Scientific Machine Learning
This talk is a short introduction to Scientific Machine Learning, a field that combines machine learning with scientific modeling. In the first part, I will explain the basics of neural networks, how they are trained using optimization, and how the backpropagation algorithm works. In the second part, I will introduce three methods used to learn or solve systems that change over time: Neural ODEs, PINNs, and DeepONets. In the final part, I will present the LED algorithm, which is designed to speed up simulations by learning simpler models that capture the key behavior of complex systems.
Mirko Bacani (Attocude systems). Structure of Innovation
Innovation has for decades been recognized as the main driver of technological advancement and – in turn – also as a major facilitator of the betterment of society at large. Yet, its structure remains elusive, and its occurrence is indeterministic and often emergent. Harnessing innovation requires understanding of its (hyperfine) structure, which I will depict here using the examples drawn from more than two decades of experience with innovation management in attocube systems. Distinguishing among the discovery, the invention and the innovation is of the utmost importance. I will sketch the difference between incremental innovation and disruptive innovation, as conceived by Clayton Christensen. I will describe how the change of perspective is a prerequisite for the nucleation of innovation, and how – with the change of paradigm – the innovation can evolve into a successful scaling up of novel technologies.