Combined Computational Approach Based on Density Functional Theory and Artificial Neural Networks for Predicting The Solubility Parameters of Fullerenes

Abstract

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 C60, PC61BM, bisPC61BM, ICMA, ICBA, and PC71BM 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.

Modeling of Hansen's solubility parameters of aripiprazole, ziprasidone, and their impurities: A nonparametric comparison of models for prediction of drug absorption sites

“…In the case of δ d , the BT model was obtained with extremely poor predictability R 2 pred = 0.01, but with good calibration performance. The prediction parameters remain in agreement with the ANN models already published in the literature . Járvás et al obtained the ANN models based on 14 σ COSMO moments with the mean absolute errors of 1.09, 1.70, and 1.96 for δ d , δ p , and δ h , respectively.…”

Interfacial width and phase equilibrium in polymer-fullerene thin-films

“…4a). The additional side-chain simplifies the system by inhibiting crystallisation of this fullerene 60 and also affects miscibility with solvents and polymers 20,61,62 . In this system, both bilayer models (with Gaussian interface roughness at the buried interface and surface) and multilayer models (using multiple layers, but with Gaussian roughness at the sample surface only and zero roughness at all internal interfaces) were fitted to the reflectivity data.…”

“…If the χ parameters for the two systems are referenced to the same volume we obtain a ratio of these χ PCBM/PS /χ bis-PCBM/PS , of 1.7. Dispersion forces are expected to predominate in these systems 62 , giving increased mixing for increased chemical similarity between species. This is evidenced experimentally by the report of increased mixing in polymer/fullerene systems (using polymers with non-conjugated backbones mixed with either C 60 or PCBM) as side-groups with increasing aromatic character are incorporated within the polymer 61 .…”

Impact of Varying Binary Solvent Gradients on the Solubility Parameters of Poly(3‐hexylthiophene)

“…These endeavors have fostered a renewed interest in solubility parameters and their application to polymeric materials. However, current methods for estimating the solubility parameters of conjugated polymers and organic semiconductor moieties, for example, fullerene and fullerene derivatives, suffer from theoretical and computational deficiencies that have led to many questions and investigations that seek improvement in the solubility parameter theory of these compounds …”