Chung, H.; Kim, D. W.; Cho, W. J.; Han, H. N.; Ikeda, Y.; Ishibashi, S.; Körmann, F.; Sohn, S. S.: Effect of solid-solution strengthening on deformation mechanisms and strain hardening in medium-entropy V1-xCrxCoNi alloys. Journal of Materials Science & Technology 108, pp. 270 - 280 (2022)
Yang, D.-C.; Jo, Y.-H.; Ikeda, Y.; Körmann, F.; Sohn, S. S.: Effects of cryogenic temperature on tensile and impact properties in a medium-entropy VCoNi alloy. Journal of Materials Science & Technology 90, pp. 159 - 167 (2021)
Ikeda, Y.; Körmann, F.: Impact of N on the Stacking Fault Energy and Phase Stability of FCC CrMnFeCoNi: An Ab Initio Study. Journal of Phase Equilibria 42, pp. 551 - 560 (2021)
Ikeda, Y.; Tanaka, I.; Neugebauer, J.; Körmann, F.: Impact of interstitial C on phase stability and stacking-fault energy of the CrMnFeCoNi high-entropy alloy. Physical Review Materials 3 (11), 113603 (2019)
Ikeda, Y.; Grabowski, B.; Körmann, F.: Ab initio phase stabilities and mechanical properties of multicomponent alloys: A comprehensive review for high entropy alloys and compositionally complex alloys. Materials Characterization 147, pp. 464 - 511 (2019)
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
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.