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Organic materials in silico

From force field development to predicting dielectric properties
PhD ceremony:S. (Selim) Sami, MSc
When:December 11, 2020
Start:11:00
Supervisors:prof. dr. H.B. (Ria) Braam, prof. dr. J.C. (Kees) Hummelen
Co-supervisor:dr. R.W.A. (Remco) Havenith
Where:Academy building RUG
Faculty:Science and Engineering
Organic materials in silico

Organic electronics have a wide range of applications, from utilizing the solar energy for electricity generation to devices that seamlessly integrate with biological surfaces. The virtually unlimited chemical space of organic molecules, while offering the possibility of ideal molecules for each of these applications, also makes it more challenging to find them. A common approach to navigating through this vast chemical space towards better performing devices is identifying design rules by correlating changes in molecular or morphological structure to the improvement of specific properties. Functionalizing organic molecules with polar side chains is one such design rule that has become a ubiquitous strategy in the search for the next generation organic materials.

This thesis elucidates, by advancing and applying computational methods, what happens at the molecular level by the inclusion of polar side chains and provides a deeper understanding of the interplay between molecular structure and dielectric and electronic properties, with the aim of guiding the field towards engineering better performing devices. A strong emphasis is given to both the accurate computation of the dielectric constant and the understanding of the relevant dielectric contributions for organic electronics. Additionally, the computational method introduced in this thesis, which is readily applicable in various materials science and biophysics studies, allows approaching quantum mechanical accuracy using computationally much more feasible molecular dynamics simulations. Overall, the findings of this thesis contribute to the goal of materials design based on computational approaches by improving existing models as well as the understanding of several property-structure relationships.