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September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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Critical assessment of QTAIM descriptors of intermolecular interactions at the promolecular level

Structural Chemistry. 2025. Vol. 36. P. 1649–1666.
Romanenko A., Ivan V. Ananyev

Intermolecular interactions play a pivotal role in chemical processes such as catalysis, crystal formation, and drug-protein complexation. The Quantum Theory of Atoms in Molecules (QTAIM) provides a robust framework for analyzing these inter actions through topological descriptors of electron density. However, the computational cost of obtaining accurate electron density distributions for large systems remains a challenge. This study critically evaluates the promolecular approximation (Independent Atom Model, IAM) as a cost-effective alternative for QTAIM analysis, focusing on its ability to describe various non-covalent interactions, including hydrogen bonds, halogen bonds, π…π stacking, and dispersion interactions. By comparing promolecular and density functional theory (DFT) results across diverse molecular systems, we demonstrate that the IAM model reliably reproduces trends in QTAIM descriptors, particularly for weaker and medium-strength interac tions. However, in the case of some types of non-directional interactions, the molecular graph is often incorrectly predicted. Furthermore, we propose a semi-quantitative model to estimate intermolecular binding energies using promolecular-derived descriptors, showcasing the potential of IAM for large-scale applications in supramolecular chemistry and materials science

Research target: Chemistry
Language: English
DOI
Keywords: electron densitynon-covalent interactionsPromoleculeIndependent atom modelQuantum Theory of Atoms in Molecules
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