The ongoing digitalization is rapidly changing and will further revolutionize all parts of life. This statement is currently omnipresent in the media as well as in the scientific community; however, the exact consequences of the proceeding digitalization for the field of materials science in general and the way research will be performed in the future are still unclear. There are first promising examples featuring the potential to change discovery and development approaches toward new materials. Nevertheless, a wide range of open questions have to be solved in order to enable the so‐called digital‐supported material research. The current state‐of‐the‐art, the present and future challenges, as well as the resulting perspectives for materials science are described.

In this work, we present a new strategy to engineer novel self-healing ionomers, namely, zwitterionic polymers, and a comprehensive analysis of their mechanical, viscoelastic, and scratch-healing properties. This new method enables reproducible damage of the polymer surfaces, calculation of the scratch volume through tactile profile scans, and quantification of the self-healing efficiency. Based on the results of the scratch tests and complementary rheology, differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and hardness tests, new trends, and structure-property relationships can be identified.

The automated dialysis of polymers in synthetic robots is described as a first approach for the purification of polymers using an automated protocol. For this purpose, a dialysis apparatus was installed within a synthesis robot. Therein, the polymer solution could be transferred automatically into the dialysis tube. Afterwards, a permanent running dialysis could be started, enabling the removal of residual monomer. Purification efficiency was studied using chromatography and NMR spectroscopy, showing that the automated dialysis requires less solvent and is faster compared to the classical manual approach.

The supramolecular halogen bonding (XB) is utilized for the first time for the preparation of shape‐memory polymers. For this purpose, an iodotriazole‐based bidentate XB donor featuring a methacrylamide is synthesized. Free radical polymerization of the XB donor monomer together with butyl methacrylate, triethylene glycole dimethacrylate, and methacrylic acid results in covalently cross‐linked polymer networks bearing both, halogen bond acceptors and donors, in their side chains. While the reversible halogen bond interactions can act as switching unit, the required stable phase of the shape‐memory polymers is formed by covalent cross‐links. The successful formation of the supramolecular cross‐links is proven via Fourier‐transform Raman spectroscopy. Furthermore, the thermal properties are investigated via differential scanning calorimetry and thermo gravimetric analysis. Thermo‐mechanical analysis reveals excellent shape‐memory abilities with fixity rates above 95% and recovery rates up to 99%. Moreover, it is possible to 3D‐print this kind of material exhibiting the ability to recover its shape within a few seconds at 130 °C.

Particle sizes represent one of the key factors influencing the usability and specific targeting of nanoparticles in medical applications such as vectors for drug or gene therapy. A multi-layered graph convolutional network combined with a fully connected neuronal network is presented for the prediction of the size of nanoparticles based only on the polymer structure, the degree of polymerization, and the formulation parameters. The model is capable of predicting particle sizes obtained by nanoprecipitation of different poly(methacrylates). This includes polymers the network has not been trained with, indicating the high potential for generalizability of the model. By utilizing this model, a significant amount of time and resources can be saved in formulation optimization without extensive primary testing of material properties.