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.

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.

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.

An automated synthesis protocol is developed for the synthesis of block copolymers in a multi-step approach in a fully automated manner. For this purpose, an automated dialysis setup is combined with robot-based synthesis protocols. Consequently, several block copolymerizations are executed completely automated and compared to the respective manual synthesis. As a result, this study opens up the field of autonomous multi-step reactions without any human interactions.

A small, low-cost, self-produced photometer is implemented into a synthesis robot and combined with a modified UV chamber to enable automated sampling and online characterization. In order to show the usability of the new approach, two different reversible addition–fragmentation chain transfer (RAFT) polymers were irradiated with UV light. Automated sampling and subsequent characterization revealed different reaction kinetics depending on polymer type. Thus, a long initiation time (20 min) is required for the end-group degradation of poly(ethylene glycol) ether methyl methacrylate (poly(PEGMEMA)), whereas poly(methyl methacrylate) (PMMA) is immediately converted. Lastly, all photometric samples are characterized via size-exclusion chromatography using UV and RI detectors to prove the results of the self-produced sensor and to investigate the molar mass shift during the reaction.