Side-chain-induced changes in aminated chitosan: Insights from molecular dynamics simulations.

Chitosan is a functional polymer with diverse applications in biomedicine, agriculture, water treatment, and beyond. Via derivatization of pristine chitosan, its functionality can be tailored to desired applications, e.g.

Twisting of Porphyrin by Assembly in a Metal-Organic Framework yielding Chiral Photoconducting Films for Circularly-Polarized-Light Detection.

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.

Graph neural networks for materials science and chemistry.

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.