Laboratory of Multiscale Modeling of Molecular Liquids and Solutions

Today, computer modeling (molecular dynamics, Monte Carlo, quantum chemistry), theoretical methods (self-consistent field theory, field-theoretic approaches, classical density functional theory and theory of integral equations), as well as deep machine learning methods have become powerful tools for studying molecular liquids and solutions along with experimental methods. These methods are usually used to describe the thermodynamic, mechanical, rheological and transport properties of molecular systems in bulk and in conditions of limited geometry, as well as to predict new materials based on the processing of large physico-chemical data using neural networks. The scientific activity of the laboratory department is aimed at the application and development of modern methods of computer modeling of molecular liquids and solutions in the volume and at the boundaries of the phases, based on the achievements of modern theoretical physics and applied mathematics, primarily methods of statistical physics of molecular liquids and solutions (theory of classical density functional, theory of self-consistent field, theory of integral equations), molecular dynamics, quantum theory and deep machine learning.