Quantum Chemistry-Machine Learning Approach for Predicting Properties of Lewis Acid-Lewis Base Adducts.

Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built from a dataset of over 1000 adducts, show exceptional performance in predicting charge transfer and other optoelectronic properties with a Pearson correlation coefficient of up to 0.99. More importantly, the influence of each molecular descriptor on predicted properties can be quantitatively evaluated from ML models. This contributes to the optimization of a priori design of Lewis adducts for future applications, especially in organic electronics.

Experimental and computational investigation of a green Knoevenagel condensation catalyzed by zeolitic imidazolate framework-8.

ZIF-8 is a highly porous, stable, and abundant surface area material that can be used as an environmentally friendly catalyst for Knoevenagel condensations. The effects of the ratio of the reactants (benzaldehyde (BA):ethyl cyanoacetate (ECA)), reaction temperature, and catalyst concentration were systematically investigated using a ZIF-8 catalyst and water as the solvent. ZIF-8 (3-5 wt%) showed excellent catalytic performance with an almost complete conversion of BA in less than 6 h with a BA:ECA molar ratio of 1:2 at different temperatures. At 60 °C, the BA conversion rate and product selectivity of the reaction reached their highest values after 4 h with a BA:ECA molar ratio of 1:1. When employing 5.0 wt% ZIF-8, almost complete BA conversion was achieved after 3 h at room temperature. ZIF-8 also demonstrated good recyclability with almost no change in its catalytic activity over five cycles. The proposed reaction mechanism is based on the catalytic activity of the basic N sites on the surface of ZIF-8, and is supported by density functional theory calculations. The present approach provides a promising strategy for the construction of simple and environmentally friendly ZIF-8 catalysts.