Physicists from Russia and Brazil Unveil Mystery behind Complex Superconductor Patterns

The mechanism is described by the Ginzburg–Landau equation at the Bogomolny point
Scientists at HSE MIEM and MIPT have demonstrated that highly complex spatial structures, similar to the intricate patterns found in nature, can emerge in superconductors. Mathematically, these patterns are described using the Ginzburg–Landau equation at a specific combination of parameters known as the Bogomolny point. The paper has been published in the Journal of Physics: Condensed Matter.
One of the most intriguing and not fully understood questions in science is how seemingly simple natural laws give rise to complex patterns, such as zebra stripes or fish scales.
Scientists have long been trying to understand how such patterns emerge in nature. The first explanation was offered in 1952 by mathematician Alan Turing, one of the founders of computer science. According to Turing, complex patterns arise from the competition between simple interactions within a system. Thus, in chemical reactions, patterns are formed through two main mechanisms: diffusion (the distribution of substances) and autocatalysis (where the reaction accelerates itself). It soon became clear that while the Turing model can also describe the emergence of complex patterns in biology effectively, it does not account for all natural phenomena.
Scientists at HSE and MIPT, in collaboration with physicists at Universidade Federal de Pernambuco, Brazil, found that the formation of complex patterns in nature can also be explained using the Ginzburg–Landau equation that describes how the state of a superconductor changes in response to a magnetic field.
A superconductor is a material that conducts electric current without resistance, meaning there is no loss of electricity. Under the influence of a magnetic field, superconductors can exhibit different behaviours: they can either completely expel the magnetic field or allow it to penetrate their mass, forming spatial structures such as a lattice of vortices.
However, according to the theory of superconductivity, there exists a special combination of superconductor parameters—referred to as the Bogomolny point—where any structure can emerge. In this study, the scientists investigated how a magnetic field changes in response to external conditions near the Bogomolny point.
Co-author of the paper, Professor, MIEM HSE
An infinite variety of intricate configurations, like monsters, lie dormant at the Bogomolny point, waiting to be unleashed as you move away from it. Depending on how you move away from it, certain types of configurations emerge. There are various methods to move away: altering the temperature, adjusting the sample size, initiating an electric current, or stacking two superconductors atop each other. This will produce a vast array of exotic patterns.
For example, structures emerge in superconductors where regions devoid of a magnetic field coexist with regions where the magnetic field forms lattices of vortices. A superconducting film can give rise to extremely exotic patterns resembling the distribution of cases in the COVID-19 pandemic.
Co-author of the paper, Professor, MIEM HSE
Previously, superconductivity was not considered a phenomenon where complex patterns could occur, as a superconductor is a relatively simple physical system. However, it turns out that highly complex magnetic structures can indeed manifest in superconductors. Our research contributes to the current understanding of how complex patterns emerge in seemingly simple systems.
The scientists suggest that the effects observed in superconductors could be used in the development of measuring instruments. For instance, by monitoring changes in configurations within a superconductor, one can gauge the extent of temperature, current, or geometric alterations in the sample.
Vasily Stolyarov
Co-author of the paper, Director, Centre for Advanced Mesoscience and Nanotechnology, MIPT
Research in this field has been ongoing from both theoretical and experimental perspectives, as well as from a technological standpoint. Since 2018, we have been the pioneers in conducting and publishing a series of experimental studies that led to the discovery and description of the process of pattern formation on the mesoscopic scale in ferromagnetic superconductors. Now, we are actively searching for and devising new systems where superconducting patterns can be controlled, thus enabling their application in nanotechnology and nanodevices.
See also:
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
Pocket Money, Personal Interest, and Family Practices: What Shapes Students’ Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
The 'Second Shift' Is Not Why Women Avoid News
Women are more likely than men to avoid political and economic news, but the reasons for this behaviour are linked less to structural inequality or family-related stress than to personal attitudes and the emotional perception of news content. This conclusion was reached by HSE researchers after analysing data from a large-scale survey of more than 10,000 residents across 61 regions of Russia. The study findings have been published in Woman in Russian Society.
Resource Race and Green Transition: Three Unexpected Conclusions from Foresight Centre’s Research on Climate and Poverty
Beneath the surface of green energy—which most people associate with solar panels, electric vehicles, and reduced CO2 emissions—lies a complex web of geopolitical interests, international inequality, and resource constraints. Researchers from the Laboratory for Science and Technology Studies (LST) at the HSE ISSEK Foresight Centre have published a series of articles in leading international journals on hidden and overt conflicts surrounding critically important metals and minerals, as well as related processes in the energy sector.
Immersion in Second Language Environment Influences Bilinguals’ Perception of Emotions
Researchers at the Cognitive Health and Intelligence Centre at the HSE Institute for Cognitive Neuroscience have discovered how bilingual individuals process emotional words in their native (first) and non-native (second) languages. It was found that the link between word meaning and bodily sensations is weaker in a second language than in a first language. However, the more a person is immersed in a language environment, the smaller this difference becomes. The article has been published in Language, Cognition and Neuroscience.
Researchers Find More Effective Approach to Revealing Majorana Zero Modes in Superconductors
An international team of researchers, including physicists from HSE MIEM, has demonstrated that nonmagnetic impurities can help more accurately reveal Majorana zero modes—quantum states considered promising building blocks for quantum computing. The researchers found that these impurities shift the energy levels that typically obscure the Majorana signal, while leaving the mode itself largely unaffected, thereby making its spectral peak more distinct. The study has been published in Research.


