Koster, D.; Žeradjanin, A. R.; Battistel, A.; La Mantia, F.: Extracting the kinetic parameters of the hydrogen evolution reaction at Pt in acidic media by means of dynamic multi-frequency analysis. Electrochimica Acta 308, pp. 328 - 336 (2019)
Žeradjanin, A. R.: Frequent Pitfalls in the Characterization of Electrodes Designed for Electrochemical Energy Conversion and Storage. ChemSusChem 11 (8), pp. 1278 - 1284 (2018)
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.