Technical Physics
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- 2023
- 2022
Research of feedforward neural network applicability in computer simulation of polymers
Shein D. V.1, Zav'yalov D. V.
1, Konchenkov V. I.
1
1Volgograd State Technical University, Volgograd, Russia
Email: danil.shein2013@yandex.ru, sinegordon@gmail.com, kontchenkov@yandex.ru
In this paper we investigate the adequacy of deep learning force field models for modeling amorphous bodies. A polymer with the studied physical properties, polyphenylene sulfide, was chosen as a test substance. The simulation results shows that the forces predicted by neural networks acting on polymer atoms are significantly different from the forces calculated by ab initio molecular dynamics methods. A qualitative comparison with the force field model of a simpler compound, black phosphorene, shows that feedforward neural networks are unsuitable for modeling complex amorphous substances. Keywords: Molecular dynamics, feedforward neural networks, force fields, polyphenylene sulfide, black phosphorene.
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