A Neural Network Approach for Property Determination of Molecular Solar Cell Candidates

Solar Efficiency of Azo-Photoswitches for Energy Conversion: A Comprehensive Assessment

Photoswitches can absorb solar photons and store them as chemical energy by photoisomerization, which is regarded as a promising strategy for photochemical solar energy storage. Although many efforts have been devoted to photoswitch discovery, the solar efficiency, a critical fundamental parameter assessing the solar energy conversion ability, has attracted little attention and remains to be studied comprehensively. Here we provide a systematic evaluation of the solar efficiency of typical azo-switches including azobenzenes and azopyrazoles, and gain a comprehensive understanding on its decisive factors. All the efficiencies are found below 1.0 %, far from the proposed limits for molecular solar thermal energy storage systems. Azopyrazoles exhibit remarkably higher solar efficiencies (0.59-0.94 %) than azobenzenes (0.11-0.43 %), benefiting from largely improved quantum yield and photoisomerization yield. Light filters can be used to improve the isomerization yield but inevitably narrow the usable range of solar spectrum, and these two contradictory effects ultimately reduce solar efficiencies. We envision this conflict could be resolved through developing azo-switches that afford high isomerization yields by absorbing wide-spectrum solar energy. We hope this work could promote more efforts to improve the solar efficiency of photoswitches, which is highly relevant to the prospect for future applications.

Machine-Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data

Knowledge of the glass transition temperature of molecular compounds that occur in atmospheric aerosol particles is important for estimating their viscosity, as it directly influences the kinetics of chemical reactions and particle phase state. While there is a great diversity of organic compounds present in aerosol particles, for only a minor fraction of them experimental glass transition temperatures are known. Therefore, we have developed a machine learning model designed to predict the glass transition temperature of organic molecular compounds based on molecule-derived input variables. The extremely randomized trees (extra trees) procedure was chosen for this purpose. Two approaches using different sets of input variables were followed. The first one uses the number of selected functional groups present in the compound, while the second one generates descriptors from a SMILES (Simplified Molecular Input Line Entry System) string. Organic compounds containing carbon, hydrogen, oxygen, nitrogen, and halogen atoms are included. For improved results, both approaches can be combined with the melting temperature of the compound as an additional input variable. The results show that the predictions of both approaches show a similar mean absolute error of about 12-13 K, with the SMILES-based predictions performing slightly better. In general, the model shows good predictive power considering the diversity of the experimental input data. Furthermore, we also show that its performance exceeds that of previous parameterizations developed for this purpose and also performs better than existing machine learning models. In order to provide user-friendly versions of the model for applications, we have developed a web site where the model can be run by interested scientists via a web-based interface without prior technical knowledge. We also provide Python code of the model. Additionally, all experimental input data are provided in form of the Bielefeld Molecular Organic Glasses (BIMOG) database. We believe that this model is a powerful tool for many applications in atmospheric aerosol science and material science.