1 research outputs found
Combined Computational Approach Based on Density Functional Theory and Artificial Neural Networks for Predicting The Solubility Parameters of Fullerenes
The solubility of organic semiconductors
in environmentally benign
solvents is an important prerequisite for the widespread adoption
of organic electronic appliances. Solubility can be determined by
considering the cohesive forces in a liquid via Hansen solubility
parameters (HSP). We report a numerical approach to determine the
HSP of fullerenes using a mathematical tool based on artificial neural
networks (ANN). ANN transforms the molecular surface charge density
distribution (σ-profile) as determined by density functional
theory (DFT) calculations within the framework of a continuum solvation
model into solubility parameters. We validate our model with experimentally
determined HSP of the fullerenes C<sub>60</sub>, PC<sub>61</sub>BM,
bisPC<sub>61</sub>BM, ICMA, ICBA, and PC<sub>71</sub>BM and through
comparison with previously reported molecular dynamics calculations.
Most excitingly, the ANN is able to correctly predict the dispersive
contributions to the solubility parameters of the fullerenes although
no explicit information on the van der Waals forces is present in
the σ-profile. The presented theoretical DFT calculation in
combination with the ANN mathematical tool can be easily extended
to other π-conjugated, electronic material classes and offers
a fast and reliable toolbox for future pathways that may include the
design of green ink formulations for solution-processed optoelectronic
devices