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

Journal article
(Original article)


Publication Details

Author(s): Perea Ospina JD, Langner S, Salvador MF, Kontos J, Jarvas G, Winkler F, Machui F, Görling A, Dallos A, Ameri T, Brabec C
Journal: Journal of Physical Chemistry B
Publication year: 2016
Volume: 120
Journal issue: 19
Pages range: 4431-4438
ISSN: 1520-6106
eISSN: 1520-5207
Language: English


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 C, PCBM, bisPCBM, ICMA, ICBA, and PCBM 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.



FAU Authors / FAU Editors

Ameri, Tayebeh Dr.
Institute Materials for Electronics and Energy Technology (i-MEET)
Brabec, Christoph Prof. Dr.
Institute Materials for Electronics and Energy Technology (i-MEET)
Görling, Andreas Prof. Dr.
Lehrstuhl für Theoretische Chemie
Langner, Stefan
Institute Materials for Electronics and Energy Technology (i-MEET)
Perea Ospina, Jose Dario
Institute Materials for Electronics and Energy Technology (i-MEET)
Salvador, Michael Filipe Dr.
Institute Materials for Electronics and Energy Technology (i-MEET)


Additional Organisation
Exzellenz-Cluster Engineering of Advanced Materials


External institutions with authors

Bayerisches Zentrum für Angewandte Energieforschung e.V. (ZAE Bayern)
University of Pannonia / Pannon Egyetem


Research Fields

B Nanoelectronic Materials
Exzellenz-Cluster Engineering of Advanced Materials
A3 Multiscale Modeling and Simulation
Exzellenz-Cluster Engineering of Advanced Materials


How to cite

APA:
Perea Ospina, J.D., Langner, S., Salvador, M.F., Kontos, J., Jarvas, G., Winkler, F.,... Brabec, C. (2016). Combined Computational Approach Based on Density Functional Theory and Artificial Neural Networks for Predicting the Solubility Parameters of Fullerenes. Journal of Physical Chemistry B, 120(19), 4431-4438. https://dx.doi.org/10.1021/acs.jpcb.6b00787

MLA:
Perea Ospina, Jose Dario, et al. "Combined Computational Approach Based on Density Functional Theory and Artificial Neural Networks for Predicting the Solubility Parameters of Fullerenes." Journal of Physical Chemistry B 120.19 (2016): 4431-4438.

BibTeX: 

Last updated on 2019-04-06 at 15:23