Sun, B.; Zhao, H.; Dong, X.; Teng, C.; Zhang, A.; Kong, S.; Zhou, J.; Zhang, X.; Tu, S.-T.: Current challenges in the utilization of hydrogen energy-a focused review on the issue of hydrogen-induced damage and embrittlement. Advances in Applied Energy 14, 100168 (2024)
Saksena, A.; Sun, B.; Dong, X.; Khanchandani, H.; Ponge, D.; Gault, B.: Optimizing site-specific specimen preparation for atom probe tomography by using hydrogen for visualizing radiation-induced damage. International Journal of Hydrogen Energy 50 (Part A), pp. 165 - 174 (2024)
Elkot, M.; Sun, B.; Zhou, X.; Ponge, D.; Raabe, D.: On the formation and growth of grain boundary k-carbides in austenitic high-Mn lightweight steels. Materials Research Letters 12 (1), pp. 10 - 16 (2024)
Shi, H.; Nandy, S.; Cheng, H.; Sun, B.; Ponge, D.: In-situ investigation of the interaction between hydrogen and stacking faults in a bulk austenitic steel. Acta Materialia 262, 119441 (2024)
Guo, Y.; Hu, J.; Han, Q.; Sun, B.; Wang, J.; Liu, C.: Microstructure diversity dominated by the interplay between primary intermetallics and eutectics for Al–Ce heat-resistant alloys. Journal of Alloys and Compounds 899, 162914 (2022)
Wang, X.; Liu, C.; Sun, B.; Ponge, D.; Jiang, C.; Raabe, D.: The dual role of martensitic transformation in fatigue crack growth. Proceedings of the National Academy of Sciences of the United States of America 119 (9), e2110139119 (2022)
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
Complex simulation protocols combine distinctly different computer codes and have to run on heterogeneous computer architectures. To enable these complex simulation protocols, the CM department has developed pyiron.
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.
Atom probe tomography (APT) is one of the MPIE’s key experiments for understanding the interplay of chemical composition in very complex microstructures down to the level of individual atoms. In APT, a needle-shaped specimen (tip diameter ≈100nm) is prepared from the material of interest and subjected to a high voltage. Additional voltage or laser…