80 citations · 281 across the 22 of their papers we have counts for
23 papers
A machine learning framework for quantifying chemical segregation and microstructural features in atom probe tomography data
Alaukik Saxena, Nikita Polin, Navyanth Kusampudi +7
Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of chemical segregation and microstructure in modern multicomponent materials. Yet, the q…
Direct Motif Extraction from High Resolution Crystalline STEM Images
Amel Shamseldeen Ali Alhassan, Siyuan Zhang, Benjamin Berkels
During the last decade, automatic data analysis methods concerning different aspects of crystal analysis have been developed, e.g., unsupervised primitive unit cell extraction and…
Three-Dimensional Damage Characterisation in Dual Phase Steel using Deep Learning
Setareh Medghalchi, Ehsan Karimi, Sang-Hyeok Lee +3
High performance sheet metals with a multi-phase microstructure suffer from deformation induced damage formation during forming in the constituent phases but importantly also where…
Tailoring the plasticity of topologically close-packed phases via the crystals' fundamental building blocks
Wei Luo, Zhuocheng Xie, Siyuan Zhang +10
Brittle topologically close-packed precipitates form in many advanced alloys. Due to their complex structures little is known about their plasticity. Here, we present a strategy to…
Constructing phase diagrams for defects by correlated atomic-scale characterization
Xuyang Zhou, Prince Mathews, Benjamin Berkels +10
Phase transformations and crystallographic defects are two essential tools to drive innovations in materials. Bulk materials design via tuning chemical compositions has been system…
Cell tracking for live-cell microscopy using an activity-prioritized assignment strategy
Karina Ruzaeva, Jan-Christopher Cohrs, Keitaro Kasahara +3
Cell tracking is an essential tool in live-cell imaging to determine single-cell features, such as division patterns or elongation rates. Unlike in common multiple object tracking,…