by Prof. Sergey Lyulin, Russian Academy of Sciences St. Petersburg, Russia

Abstract:

*Atomistic microsecond molecular dynamics simulation is a powerful approach
to evaluate the physical properties of various polymer systems of complex
structure, including heterocyclic polymers and nanocomposites based on
them. Thermal, transport, mechanical and structural properties of various
polyimides, including semi-crystallizable ones, can be effectively
predicted. Special attention will be paid to the problem of initial
equilibration and comparison with known experimental data, which is a
serious problem due to the huge difference in cooling and elongation rates
in real experiment and in simulations.*

*Machine learning (ML) represents another theoretical approach in
computational material science which yields the modern paradigm of virtual
material design based on known experimental data. Focusing on polyimides
(PI), which are high-performance heterocyclic polymers with big variance of
chemical structure we developed graph convolutional neural network (GCNN),
being one of the most promising tools for working with big-data, to predict
their glass transition temperature (Tg) as an example of the most
fundamental polymer property. *
*This work has been financially supported by by the Russian Science
Foundation, grant No. 22-13-00066.*