4 papers
SAGE: Surrogate-gradient Adaptation via Attention-Guided Entropy for Spiking Transformers
Kiran Nair, Rodrigue Rizk, KC Santosh
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep neural networks by exploiting sparse event-driven computation, but their training remains…
Predicting Grain Boundary Segregation in Magnesium Alloys: An Atomistically Informed Machine Learning Approach
Zhuocheng Xie, Achraf Atila, Julien Guénolé +3
Grain boundary (GB) segregation in magnesium (Mg) substantially influences its mechanical properties and performance. Atomic-scale modelling, typically using ab-initio or semi-empi…
Solute Co-Segregation Mechanisms at Low-Angle Grain Boundaries in Magnesium: A Combined Atomic-Scale Experimental and Modeling Study
Risheng Pei, Joé Petrazoller, Achraf Atila +10
Solute segregation at low-angle grain boundaries (LAGBs) critically affects the microstructure and mechanical properties of magnesium (Mg) alloys. In modern alloys containing multi…
Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence
Tom Reclik, Setareh Medghalchi, Philipp Schumacher +4
Scanning Electron Microscopy (SEM) is pivotal in revealing intricate micro- and nanoscale features across various research fields. However, obtaining high-resolution SEM images pre…