Isotta, E.: Investigating microstructure via thermal conductivity imaging: from grain boundaries, to phase segregations and material anisotropy. 50th International Conference and Expo on Advanced Ceramics and Composites (ICACC 2026), Daytona Beach, FL, USA (2026)
Isotta, E.: Investigating microstructure via thermal conductivity imaging: from grain boundaries, to material anisotropy, and phase segregations. Invited Seminar at RWTH Aachen, Physics Department, Aachen, Germany (2025)
Isotta, E.: Thermal conductivity imaging to advance microstructure engineering in thermoelectric and energy materials. Materials Science and Technology Meeting (MSandT) 2025, Columbus, OH, USA (2025)
Isotta, E.; Zhang, S.; Ghosh, S.; de Boor, J.; Balogun, O.; Snyder, G. J.; Scheu, C.: Thermal conductivity imaging to advance microstructure engineering in thermoelectrics. European Conference on Thermoelectrics 2025, Nancy, France (2025)
Isotta, E.: Thermal conductivity imaging to guide microstructure engineering in energy materials. Invited Seminar at the Karlsruhe Institute of Technology, Karlsruhe, Germany (2025)
Isotta, E.: Thermal conductivity imaging to guide microstructure engineering in energy materials. Invited Seminar at the German Aerospace Center in Cologne, Köln, Germany (2025)
Isotta, E.: Thermal conductivity imaging to guide microstructure engineering in energy materials. Iberian Workshop on Thermoelectrics 2025, Castello de la Plana, Spain (2025)
Isotta, E.: Local thermal conductivity imaging and modelling to guide microstructure engineering in energy materials. TMS 2025 Annual Meeting, Las Vegas, NV, USA (2025)
Isotta, E.: Thermal conductivity imaging to guide microstructure engineering in energy materials. Invited Seminar at the Institute of Science and Technology Austria, Klosterneuburg, Austria (2024)
Busch, F.; Balogun, O.; Snyder, G. J.; Scheu, C.; Isotta, E.: Unravelling grain boundary influences on electronic and lattice thermal conductivity in Mn-doped SnTe thermoelectrics. 21st European Conference on Thermoelectrics (ECT) 2025, Nancy, Frankreich (2025)
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
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.
New product development in the steel industry nowadays requires faster development of the new alloys with increased complexity. Moreover, for these complex new steel grades, it is more challenging to control their properties during the process chain. This leads to more experimental testing, more plant trials and also higher rejections due to…
The general success of large language models (LLM) raises the question if they could be applied to accelerate materials science research and to discover novel sustainable materials. Especially, interdisciplinary research fields including materials science benefit from the LLMs capability to construct a tokenized vector representation of a large…