New Method for Describing Graphene Simplifies Analysis of Nanomaterials
An international team, including scientists from HSE University, has proposed a new mathematical method to analyse the structure of graphene. The scientists demonstrated that the characteristics of a graphene lattice can be represented using a three-step random walk model of a particle. This approach allows the lattice to be described more quickly and without cumbersome calculations. The study has been published in Journal of Physics A: Mathematical and Theoretical.
Graphene is one of the most recent and widely discussed materials of the 21st century. It consists of a single layer of carbon atoms arranged in a honeycomb, hexagonal lattice structure. Graphene is exceptionally strong, an excellent conductor of electricity, nearly transparent, and highly flexible. It has already been used in the production of conductive films, sensors, and miniature transistors. Similar structures occur in other carbon forms, such as fullerenes—closed spherical molecules composed of pentagons and hexagons—used in drug delivery and solar cell technology. These materials have numerous structural variants that directly influence their properties, including molecular stability. Experimentally testing each variant is costly and difficult, so scientists are seeking simpler methods to predict their characteristics.
An international team of scientists from Germany, Russia, France, and Japan, including researchers from HSE University, has proposed such a method. They simplified the description of the key parameters that determine the lattice’s behaviour into a three-step random walk model.
In this model, an imaginary particle starts at the origin of a plane and takes three equal-length steps in random directions. The target lattice parameter is then defined by the final position of the particle along the x-axis. Mathematically, this is expressed as the sum of the cosines of three random numbers corresponding to the step directions. For the calculations, it is enough to repeatedly generate random numbers, substitute them into the formulas, and add the results. Repeating this procedure many times yields values that capture the key properties of the lattice. This method describes the material without complex computations and simplifies the analysis.
This simplified calculation method is useful not only for graphene. The authors suggest that their approach could also be applied to other carbon structures, such as fullerenes.
Victor Buchstaber
'We hypothesised that as the size of a molecule increases, random fullerenes become locally more similar in structure to an infinite graphene lattice. If this can be rigorously proven, the spectral properties of fullerenes could be derived from those of graphene, greatly simplifying their analysis,' explains Viktor Buchstaber from the International Laboratory of Algebraic Topology and Its Applications at the HSE Faculty of Computer Science.
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Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.
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Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.
HSE Researchers Create New Corpus of Early Child Speech in Russian
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Hybrid Intelligence: Competencies in the Age of AI Discussed at Technoprom-2026
Artificial intelligence is not creating new professions, but rather transforming the nature of existing ones. This was the conclusion reached by participants in the panel session ‘Hybrid Intelligence: Digital and Human Drivers of Development,’ organised by the Institute for Statistical Studies and Economics of Knowledge (ISSEK) at HSE University as part of the 13th International Forum of Technological Development (Technoprom-2026). The experts discussed how the nature of work is changing, which skills are becoming increasingly sought after, and what prevents companies from fully capitalising on new technologies.
Scientists Develop New Solution for 6G Communication Systems
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Scientists Develop Algorithm for More Reliable Processors in Data Centres
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Researchers Rank Recommendation Algorithms Using Sports Tournament Model
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed an approach for selecting recommendation algorithms more effectively. Their approach uses pairwise comparisons of algorithms to create a tournament table, with the overall ranking based on their performance across all datasets in the tournament. This can reduce the number of algorithms that need to be tested when developing new services, saving both time and money. The study was presented at the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026).
Researchers Develop Method for Direct Generation of Regulatory DNA
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Researchers at HSE University and Sber Train Neural Networks to Better Predict User Preferences
The HSE FCS AI and Digital Science Institute and Sber have introduced a new architecture for recommendation systems that combines two classes of models, enabling algorithms to better predict users’ interests and needs. A preprint of the paper has been published on arxiv.org and presented at Urban ML.
Physicists Discover What Happens Inside a Stable Vortex
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