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October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.
October 7, 2026
‘Our Team Consists of True Leaders in Their Respective Academic Disciplines
The HSE International Centre of Decision Choice and Analysis studies a wide range of methods for analysing decision-making and possible scenarios for the development of natural, socio-economic, and political phenomena using various mathematical models. The application of advanced mathematical methods to forecasting helps to prevent negative outcomes and avoid erroneous decisions. The HSE News Service spoke to the centre’s director, Prof. Fuad Aleskerov, about its work.
October 6, 2026
International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.

 

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Dynamical properties of hydrogen fluid at high pressures

Journal of Chemical Physics. 2025. Vol. 162. No. 2. Article 024504.
Gliaudelis G., Lukyanchuk V., Chtchelkatchev N., Saitov I., Kondratyuk N.

The properties of the hydrogen fluid at high pressures are still of interest to the scientific community. The experimentally unreachable dynamical properties could provide new insights into this field. In 2020 [Cheng et al., Nature 585, 217–220 (2020)], the machine-learned approach allows the calculation of the self-diffusion coefficient in the warm dense hydrogen with higher precision. After that, the work [van de Bund et al., Phys. Rev. Lett. 126(22), 225701 (2021)] reports the ab initio treatment of isotopic effects on diffusion in H2/D2 and a significant increase in its value in the region of the phase transition. Both works indicate the anomalous growth of diffusion, but the reasons for this phenomenon are unclear. In the present work, we reveal the plasma-like behavior of the diffusion growth. We apply the classical molecular dynamics method using a machine learning potential developed on the ab initio modeling for the prediction of diffusion and shear viscosity coefficients. We consider dependencies of the vibrational spectrum, molecule lifetime, diffusion, and shear viscosity coefficients on density along the isotherms in the temperature range from 600 to 1100 K.

Research target: Physics Computer Science
Language: English
DOI
Text on another site
Keywords: diffusion coefficientshear viscosityAb initio molecular dynamics (AIMD)Density functional theory (DFT)machine learning potential
Publication based on the results of:
Advanced materials and technologies (2025)
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