Yin, Y.; Zhao, H.; Prabhakar, J. M.; Rohwerder, M.: Organic composite coatings containing mesoporous silica particles: Degradation of the SiO2 leading to self-healing of the delaminated interface. Corrosion Science 200, 110252 (2022)
Yin, Y.; Schulz, M.; Rohwerder, M.: Optimizing smart self-healing coatings: Investigating the transport of active agents from the coating towards the defect. Corrosion Science 190, 109661 (2021)
Yin, Y.: Self-heating coatings based on conducting polymer for intelligent corrosion protection. Dissertation, Ruhr-Universität Bochum, Fakultät für Maschinenbau (2022)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.