Haghighat, S. M. H.; Schäublin, R. E.: Obstacle strength of binary junction due to dislocation dipole formation: An in-situ transmission electron microscopy study. Journal of Nuclear Materials 465, pp. 648 - 652 (2015)
Haghighat, S. M. H.; Schäublin, R. E.; Raabe, D.: Atomistic simulation of the a0 <1 0 0> binary junction formation and its unzipping in body-centered cubic iron. Acta Materialia 64, pp. 24 - 32 (2014)
Schäublin, R. E.; Haghighat, S. M. H.: Molecular dynamics study of strengthening by nanometric void and Cr alloying in Fe. Journal of Nuclear Materials 442 (1-3 Suppl.1), pp. S643 - S648 (2013)
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…