RESUMO
While materials based on organic molecules usually have either superior optoelectronic or superior chiral properties, the combination of both is scarce. Here, a crystalline chiroptical film based on porphyrin with homochiral side groups is presented. While the dissolved molecule has a planar, thus, achiral porphyrin core, upon assembly in a metal-organic framework (MOF) film, the porphyrin core is twisted and chiral. The close packing and the crystalline order of the porphyrin cores in the MOF film also results in excellent optoelectronic properties. By exciting the Soret band of porphyrin, efficient photoconduction with a high On-Off-ratio is realized. More important, handedness-dependent circularly-polarized-light photoconduction with a dissymmetry factor g of 4.3×10-4 is obtained. We foresee the combination of such assembly-induced chirality with the rich porphyrin chemistry will enable a plethora of organic materials with exceptional chiral and optoelectronic properties.
RESUMO
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.
