Silke Christiansen
Silke Christiansen is a full professor of physics at the Free University of Berlin since 2013 and heads the research department for Correlative Microscopy and Materials Data at the Fraunhofer Institute for Ceramic Materials and Systems (IKTS) in Forchheim, Germany. Her scientific career has been recognized with numerous awards, including the MRS Student Award, a fellowship from the Bayerische Forschungsstiftung for research at Columbia University in New York, and a Feodor Lynen Fellowship from the Alexander von Humboldt Foundation, which enabled her to conduct pioneering work in silicon technology at IBM’s T.J. Watson Research Center in Yorktown Heights. From 2015 to 2021, she also served as honorary professor in the Department of Materials Science at Chungbuk National University in Korea.
Her expertise spans nanomaterials for energy applications such as batteries and photovoltaics, advanced microscopy and spectroscopy, biomedical sensing, biotechnology, and opto-, power-, and large-area electronics as well as quantitative image analysis using ML/AI algorithms.
She advances these fields through world-class analytical infrastructure that is part of the prestigious labs@location program of Carl Zeiss Microscopy and part of a strategic partnership with Horiba Europe and Leica Microsystems. Over the course of her career, she has held research and leadership positions at leading institutions in Germany and the United States, including Columbia University, the Max Planck Institutes in Halle and Erlangen, the Helmholtz Zentrum Berlin for Materials and Energy, the Leibniz Institute for Photonic Technology in Jena, and the Friedrich-Alexander University Erlangen-Nürnberg, where she completed her PhD and habilitation. She has authored more than 430 peer-reviewed publications, holds over a dozen patents and patent applications, and her work has been cited more than 18,100 times, reflected in an h-index of 68.
Title: Multimodal Analytics and Data-Driven Optimization of Ceramic Composite Materials for Energy and Electronic Applications
Advanced ceramic composites are critical enablers of high-performance energy storage and electronic devices. To meet increasing demands for reliability, sustainability, and miniaturization, we introduce a scale-bridging, data-driven optimization pipeline that unites multimodal analytics, machine learning, and physics-based simulations. This integrative framework is broadly applicable across material systems and device platforms, with batteries and microelectronic components serving as exemplary use cases.
The pipeline leverages high-resolution, correlative data from electron, ion, and X-ray microscopies (SEM, FIB-SEM, AFM, XRM), complemented by spectroscopic modalities such as Raman, IR, and µXRF. With nanoGPS-based relocalization, we achieve true multimodal correlation across instruments and scales, enabling quantitative analysis of critical features such as interfaces, degradation zones, and functional inhomogeneities.
Acquisition workflows are designed to maintain chemical and structural integrity—e.g., using glovebox-integrated tools and contamination-free transfer holders—while operando methods provide dynamic insights under real device conditions. These rich datasets are integrated, analyzed, and visualized using the Correlyze platform, allowing AI-assisted segmentation, anomaly detection, and pattern recognition.
Validated experimental insights are coupled with multiscale simulations, including finite element modeling and AI-based surrogate modeling. This approach enables predictive assessment of functional behavior—be it ionic transport in batteries or thermal/electrical reliability in integrated circuits.
The resulting closed-loop framework streamlines the optimization of ceramic materials across diverse applications. By systematically correlating structure, composition, and performance, we accelerate innovation in both energy and electronic technologies through a unified, data-centric approach.