Lee, C.; Yun, Y. H.; Kim, S.-H.; Doo, G.; Lee, S.; Park, H.; Park, Y.; Shin, J.; Cho, H.-S.; Kim, S.-K.et al.; Cho, E.; Jung, C.; Kim, M.: Structural and Compositional Optimization of Fe–Co–Ni Ternary Amorphous Electrocatalysts for Efficient Oxygen Evolution in Anion Exchange Membrane Water Electrolysis. Small 21 (4), 2405468 (2025)
Jang, K.; Kim, M.-Y.; Jung, C.; Kim, S.-H.; Choi, D.; Park, S.-C.; Scheu, C.; Choi, P.-P.: Direct Observation of Trace Elements in Barium Titanate of Multilayer Ceramic Capacitors Using Atom Probe Tomography. Microscopy and Microanalysis 30 (6), pp. 1047 - 1056 (2024)
Yoo, B.; Jung, C.; Jang, K.; Jun, H.; Choi, P.-P.: Novel Ni-Co-based superalloys with high thermal stability and specific yield stress discovered by directed energy deposition. Materials and Design 238, 112607 (2024)
Park, H.; Jung, C.; Yi, S.; Choi, P.-P.: Elucidating the ball-milling-induced crystallization mechanism of amorphous NbCo1.1Sn via atomic-scale compositional analysis. Journal of Alloys and Compounds 968, 172014 (2023)
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.