A Bayesian Approach to Predict Solubility Parameters

Solubility is a ubiquitous phenomenon in many aspects of material science. While solubility can be determined by considering the cohesive forces in a liquid via the Hansen solubility parameters (HSP), quantitative structure-property relationship models are often used for prediction, notably due to their low computational cost. Herein, we report gpHSP, an interpretable and versatile probabilistic approach to determining HSP. Our model is based on Gaussian processes (GP), a Bayesian machine learning approach that provides uncertainty bounds to prediction. gpHSP achieves its flexibility by leveraging a variety of input data, such as SMILES strings, COSMOtherm simulations, and quantum chemistry calculations. gpHSP is built on experimentally determined HSP, including a general solvents set aggregated from literature, and a polymer set experimentally characterized by this group of authors. In all sets, we obtained a high degree of agreement, surpassing well-established machine learning methods. We demonstrate the general applicability of gpHSP to miscibility of organic semiconductors, drug compounds and in general solvents, which can be further extended to other domains. gpHSP is a fast and accurate toolbox, which could be applied to molecular design for solution processing technologies.<br

Understanding and controlling the evolution of nanomorphology and crystallinity of organic bulk-heterojunction blends with solvent vapor annealing

Solvent vapor annealing (SVA) has been shown to significantly improve the device performance of organic bulk-heterojunction solar cells, yet the mechanisms linking nanomorphology, crystallinity of the active layer, and performance are still largely missing. Here, the mechanisms are tackled by correlating the evolution of nanomorphology, crystallinity, and performance with advanced transmission electron microscopy methods systematically. Model system of DRCN5T:PC71BM blends are SVA treated with four solvents differing in their donor and acceptor solubilities. The choice of solvent drastically influences the rate at which the maximum device efficiency establishes, though similar values can be achieved for all solvents. The donor solubility is identified as a key parameter that controls the kinetics of diffusion and crystallization of the blend molecules, resulting in an inverse relationship between optimal annealing time and donor solubility. For the highest efficiency, optimum domain size and single-crystalline nature of DRCN5T fibers are found to be crucial. Moreover, the π–π stacking orientation of the crystallites is directly revealed and related to the nanomorphology, providing insight into the charge carrier transport pathways. Finally, a qualitative model relating morphology, crystallinity, and device efficiency evolution during SVA is presented, which may be transferred to other light-harvesting blends.</p

Robot-Based High-Throughput Engineering of Alcoholic Polymer: Fullerene Nanoparticle Inks for an Eco-Friendly Processing of Organic Solar Cells

Development of high-quality organic nanoparticle inks is a significant scientific challenge for the industrial production of solution-processed organic photovoltaics (OPVs) with eco-friendly processing methods. In this work, we demonstrate a novel, robot-based, high-throughput procedure performing automatic poly­(3-hexylthio-phene-2,5-diyl) and indene-C₆₀ bisadduct nanoparticle ink synthesis in nontoxic alcohols. A novel methodology to prepare particle dispersions for fully functional OPVs by manipulating the particle size and solvent system was studied in detail. The ethanol dispersion with a particle diameter of around 80–100 nm exhibits reduced degradation, yielding a power conversion efficiency of 4.52%, which is the highest performance reported so far for water/alcohol-processed OPV devices. By successfully deploying the high-throughput robot-based approach for an organic nanoparticle ink preparation, we believe that the findings demonstrated in this work will trigger more research interest and effort on eco-friendly industrial production of OPVs

Robot-Based High-Throughput Engineering of Alcoholic Polymer: Fullerene Nanoparticle Inks for an Eco-Friendly Processing of Organic Solar Cells

Development of high-quality organic nanoparticle inks is a significant scientific challenge for the industrial production of solution-processed organic photovoltaics (OPVs) with eco-friendly processing methods. In this work, we demonstrate a novel, robot-based, high-throughput procedure performing automatic poly­(3-hexylthio-phene-2,5-diyl) and indene-C₆₀ bisadduct nanoparticle ink synthesis in nontoxic alcohols. A novel methodology to prepare particle dispersions for fully functional OPVs by manipulating the particle size and solvent system was studied in detail. The ethanol dispersion with a particle diameter of around 80–100 nm exhibits reduced degradation, yielding a power conversion efficiency of 4.52%, which is the highest performance reported so far for water/alcohol-processed OPV devices. By successfully deploying the high-throughput robot-based approach for an organic nanoparticle ink preparation, we believe that the findings demonstrated in this work will trigger more research interest and effort on eco-friendly industrial production of OPVs

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 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

Introducing a New Potential Figure of Merit for Evaluating Microstructure Stability in Photovoltaic Polymer-Fullerene Blends

A theoretical understanding of the microstructure of organic semiconducting polymers and blends is vital to further advance the optoelectronic device performance of organic electronics. We outline the theoretical framework of a combined numerical approach based on polymeric solution theory to study the microstructure of polymer:small molecule blends. We feed the results of ab initio density functional theory quantum chemistry calculations into an artificial neural network for the determination of solubility parameters. These solubility parameters are used to calculate Flory–Huggings intermolecular parameters. We further show that the theoretical values are in line with experimentally determined data. On the basis of the Flory–Huggings parameters, we establish a figure of merit as a relative metric for assessing the phase diagrams of organic semiconducting blends in thin films. This is demonstrated for polymer:fullerene blend films on the basis of the prototypical polymers poly­(3-hexylthiophene-2,5-diyl) (P3HT) and poly­[(5,6-difluoro-2,1,3-benzothiadiazol-4,7-diyl)-<i>alt</i>-(3,3-di­(2-octyldodecyl)-2,2,5,2;5,2-quaterthiophen-5,5-diyl)] (PffBT4T-2OD). After confirming the applicability of our model with a broader range of materials and differences in molecular weight, we suggest that this combined model should be able to inform design criteria and processing guidelines for existing and new high performance semiconducting blends for organic electronics applications with ideal and stable solid state morphology