Sahar Sagy
Understanding Aromaticity for the Future of Semiconductors
Contrary to conventional semiconductors, which are based on highly efficient inorganic materials and entail high monetary and environmental costs, semiconductors based on organic materials offer unique possibilities. These are made possible by attributes such as the light weight, mechanical flexibility, and low-cost of production of these materials. These features make organic semiconductors promising candidates for fabricating organic field-effect transistors (OFETs), organic light-emitting diodes (OLEDs), organic photovoltaics (OPVs), liquid-crystal displays and organic thin film transistors (OTFTs). All of the abovementioned find invaluable applications in integrated circuits, sensors, and displays [1]. All known organic semiconductors are based on aromatic molecules, and more specifically polycyclic aromatic systems, either in polymeric or crystalline form.
This term “Aromaticity” is not well defined and is based on similarity between different molecules to the molecule Benzene. Therefore, there have evolved many different measurements of properties, known as indices to characterize the aromaticity of a molecule. The variety of different indices, together with the vagueness of the term aromaticity itself, often lead to conflicting evaluations and interpretations of aromaticity in the same molecule. On a secondary level, it also makes it challenging to correlate between aromaticity and other physical properties required from the organic semiconductors. The aim of my research is to use machine learning models and various aromaticity indices (obtained with computational tools) – to identify different relationships between a) the various aromaticity indices, b) aromaticity indices and molecular properties, and c) aromaticity indices and structural features. These structure-property relationships can inform new design strategies for functional compounds, specifically for new and better organic semiconductors.