6 papers
LiNiO2/NiO Phase Prediction Using Artificial Neural Networks
Jonas Scheunert, Shamail Ahmed, Thomas Demuth +2
In the realm of material analysis, identifying different material phases is of key importance. Artificial intelligence in the form of neural networks provides a very fast and, once…
Orientation Mapping via Dictionary Indexing of AC-STEM Kikuchi patterns using Open-source Software
Shamail Ahmed, Johannes Haust, Felix Gruber +5
The properties of polycrystalline materials are strongly influenced by the spatial arrangement and orientations of individual grains within the microstructure, making nanoscale cha…
Microstructure of Silicon Anodes in Solid-State Batteries -- From Crystalline to Amorphous
Shamail Ahmed, Federico Rossi, Hanyu Huo +6
Silicon offers great promise as a potential anode active material and the optimum alternative to lithium metal in all-solid-state lithium-ion batteries. However, its practical appl…
STEM Diffraction Pattern Analysis with Deep Learning Networks
Sebastian Wissel, Jonas Scheunert, Aaron Dextre +4
Accurate grain orientation mapping is essential for understanding and optimizing the performance of polycrystalline materials, particularly in energy-related applications. Lithium…
Determining the grain orientations of battery materials from electron diffraction patterns using convolutional neural networks
Jonas Scheunert, Shamail Ahmed, Thomas Demuth +4
Polycrystalline materials have numerous applications due to their unique properties, which are often determined by the grain boundaries. Hence, quantitative characterization of gra…
Measuring spatially-resolved potential drops at semiconductor hetero-interfaces using 4D-STEM
Varun Shankar Chejarla, Shamail Ahmed, Jürgen Belz +3
Characterizing long-range electric fields and built-in potentials in functional materials at nano- to micrometer scales is of supreme importance for optimizing devices. E.g., the f…