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)
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
Integrated Computational Materials Engineering (ICME) is one of the emerging hot topics in Computational Materials Simulation during the last years. It aims at the integration of simulation tools at different length scales and along the processing chain to predict and optimize final component properties.
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…
The project’s goal is to synergize experimental phase transformations dynamics, observed via scanning transmission electron microscopy, with phase-field models that will enable us to learn the continuum description of complex material systems directly from experiment.
In order to prepare raw data from scanning transmission electron microscopy for analysis, pattern detection algorithms are developed that allow to identify automatically higher-order feature such as crystalline grains, lattice defects, etc. from atomically resolved measurements.