Shi, H.; Hachet, G.; Cheng, H.; Prithiv, T. S.; Ponge, D.; Sun, B.: Improving hydrogen embrittlement resistance of martensitic steel via interface B segregation. International Journal of Hydrogen Energy 164, 150954 (2025)
Bhattacharya, A.; Barik, R. K.; Nandy, S.; Sen, M.; Prithiv, T. S.; Patra, S.; Mitra, R.; Chakrabarti, D.; Ghosh, A.: Effect of martensite twins on local scale cleavage crack propagation in a medium carbon armor grade steel. Materialia 30, 101800 (2023)
Sukumar Prithiv, T.; Gault, B.; Li, Y.; Andersen, D.; Valle, N.; Eswara, S.; Ponge, D.; Raabe, D.: Austenite grain boundary segregation and precipitation of boron in low-C steels and their role on the heterogeneous nucleation of ferrite. Acta Materialia 252, 118947 (2023)
Prithiv, T. S.; Thirumurugan, G.; Madan, M.; Kamaraj, A.: Thermodynamic Assessment of Steelmaking Practices for the Production of Re-sulfur Steels. Transactions of the Indian Institute of Metals 73 (6), pp. 1595 - 1603 (2020)
Srikakulapu, K.; Morsdorf, L.; Tung, P.-Y.; Prithiv, T. S.; Herbig, M.: Cementite decomposition in 100Cr6 bearing steel during high-pressure torsion: Influence of precipitate composition, size, morphology and matrix hardness. European Congress and Exhibition on Advanced Materials and Processes, EUROMAT 2021, online (2021)
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.