89 citations · 131 across the 13 of their papers we have counts for
14 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…
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…
Mean field approximation for effective theories of lattice QCD
Christoph Konrad, Owe Philipsen, Jonas Scheunert
For the exploration of the phase diagram of QCD, effective Polyakov loop theories derived from lattice QCD provide a valuable tool in the heavy quark mass regime. In practice, the…
QCD in the heavy dense regime: Large and quarkyonic matter
Owe Philipsen, Jonas Scheunert
After combined character and hopping expansions and integration over the spatial gauge links, lattice QCD reduces to a three-dimensional Polyakov loop model with complicate…