Conclusion

Chapter 7

Additionally, to by the chapter conclusions, I would like to highlight the most essential part of each of the chapters here.

The thesis was focused on studying several objects using different techniques:

the mean-field, the coarse-grained, the hybrid MC-SCF approach, and density functional theory with machine learning routine.

At the beginning of the thesis, star-like polymers were discussed using different resolution scales: mean-field, coarse-grained and hybrid representations. All results were in mutual agreement. However, the pure coarse-grained model the provides the most “exact” values becomes computationally expensive very quickly. And, the pure mean-field representation is less expensive in a computational way but does underestimate the values. Nevertheless, the MC-SCF method suppresses the effect of mean-filed approximation and is not as expensive as the coarse-grained model.

Thus, the comparison of purely coarse-grained and purely mean-field models showed that the hybrid method has a beneficial way of simulations. The method ap- pears as an interesting alternative for molecular dynamics simulation of polymeric systems. Moreover, the tunable inheritance makes the computational expenses a function of the resolution.

In the middle of the thesis, the hydrogel under poor solvent conditions was the focus of the story. The molecular dynamics simulation showed the phase transition of the hydrogel at specific external stimuli. By the salt concentrationc_s, hydropho- bicityε, or pH−pKdifference the phase transition can be controlled. Precisely, the phase transition can be broadened by decreasing pH−pKdifference and/or deteri- oration of the solvent quality.

Additionally, the simple analytical model was used to guide the molecular dynamics simulation. It was shown, that the analytical model overestimates the transition pressure. However, the predictions could be significantly improved by assum- ing the pH−pKdifference as a function of salt concentrationc_s. The latter means a more accurate accounting of the ionic contributions in the analytical model.

At the end of the thesis, the density functional theory and machine learning routine were discussed. The focus of the discussion was on the fullerene family and re- ducing computational expenses by introducing the machine learning potential. The potential grasp all essential insights from the training set and is able to predict the energy with error of 0.001 Ha, forces of 0.0004 Ha/Bohr, and dipole moment of 0.015 Debye. The results make the approach a promising alternative to DFT simulation.

Acknowledgements

I would like to thank all people, who have contributed to the thesis.

I owe my special gratitude to my supervisor Dr. Filip Uhlík, who gave me the opportunity to work on this topic.

I appreciate that he took always his time for discussions and guidance. This work would not have been possible without his optimism, patience, reassurance, and help. I also would like to thank Oleg Rud for his advice and great help. I thank Lucie Nová for her collaboration, optimism, and kind words. I appreciate the nice discussions during my internships with Prof. Frans Leermakers. I will remember a fantastic and productive stay in the ICP group in Stuttgart.

I am thankful to my beloved family for their care, encouragement, and infinite help in difficult times, especially to Varya for useful scientific and non-scientific discussions.

At the same time, I thank all friends for their understanding and support. Special gratitude to Sasha Chudov, who helped with nice discussions and suggestions.

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