Stereochemistry of spiro-acetalized [60]fullerenes: how the exo and endo stereoisomers influence organic solar cell performance

Electron-vibrational renormalization in fullerenes through ab initio and machine learning methods

The effect of nuclear vibrations on the electronic eigenvalues and the HOMO-LUMO gap is known for several kinds of carbon-based materials, like diamond, diamondoids, carbon nanoclusters, carbon nanotubes and others, like hydrogen-terminated oligoynes and polyyne. However, it has not been widely analysed in another remarkable kind which presents both theoretical and technological interest: fullerenes. In this article we present the study of the HOMO, LUMO and gap renormalizations due to zero-point motion of a relatively large number (163) of fullerenes and fullerene derivatives. We have calculated this renormalization using density-functional theory with the frozen-phonon method, finding that it is non-negligible (above 0.1 eV) for systems with relevant technological applications in photovoltaics and that the strength of the renormalization increases with the size of the gap. In addition, we have applied machine learning methods for classification and regression of the renormalizations, finding that they can be approximately predicted using the output of a computationally cheap ground state calculation. Our conclusions are supported by recent research in other systems.

Imaging deposition-dependent supramolecular chiral organisation

Thin films of a chiral diketopyrrolopyrrole derivative were imaged with spatially-defined Mueller Matrix Polarimetry, focussing on the Circular Dichroism signal, giving unique insight into the impact that deposition techniques and thermal annealing can have on chiral supramolecular structures in the solid state, where homogeneity was observed for spun-coated films while drop-coating afforded chiroptical diversity in the material, a feature invisible to absorption spectroscopy or optical microscopy.

Photoisomerization of dicyanorhodanine-functionalized thiophenes

π-Conjugated oligomers functionalized with the popular dicyanorhodanine (RCN) electron acceptor are shown to be susceptible to photo-induced Z/E isomerization. The stereochemistry of two model RCN-functionalized thiophenes is confirmed by single crystal X-ray analysis and 2D NMR, and shown to be the thermodynamically stable Z form. Relative energies, Z/E configurations, and conformational preferences are modelled using density functional theory (DFT). The photophysical properties of the model compounds are explored experimentally and computationally; the Z and E isomers display similar absorption profiles with significant spectral overlap and are inseparable upon irradiation to a photostationary state. The well-behaved photoisomerization process is routinely observable by thin-layer chromatography, UV-vis, and NMR, and the photochemical behavior of the two RCN-functionalized thiophenes is characterized under varying wavelengths of irradiation. Ultraviolet (254 nm) irradiation results in photostationary state compositions of 56/44 and 69/31 Z-isomer/E-isomer for substrates functionalized with one thiophene and two thiophenes, respectively. Ambient laboratory lighting results in excess of 10 percent E-isomer for each species in solution, an important consideration for processing such materials, particularly for organic photovoltaic applications. In addition, a photoswitching experiment is conducted to demonstrate the reversible nature of the photoreaction, where little evidence of fatigue is observed over numerous switching cycles. Overall, this work showcases an approach to characterize the stereochemistry and photochemical behavior of dicyanorhodanine-functionalized thiophenes, widely used components of functional molecules and materials.

Computer-Aided Screening of Conjugated Polymers for Organic Solar Cell: Classification by Random Forest

Owing to the diverse chemical structures, organic photovoltaic (OPV) applications with a bulk heterojunction framework have greatly evolved over the last two decades, which has produced numerous organic semiconductors exhibiting improved power conversion efficiencies (PCEs). Despite the recent fast progress in materials informatics and data science, data-driven molecular design of OPV materials remains challenging. We report a screening of conjugated molecules for polymer-fullerene OPV applications by supervised learning methods (artificial neural network (ANN) and random forest (RF)). Approximately 1000 experimental parameters including PCE, molecular weight, and electronic properties are manually collected from the literature and subjected to machine learning with digitized chemical structures. Contrary to the low correlation coefficient in ANN, RF yields an acceptable accuracy, which is twice that of random classification. We demonstrate the application of RF screening for the design, synthesis, and characterization of a conjugated polymer, which facilitates a rapid development of optoelectronic materials.