3 papers
cs.LG2026
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…
cond-mat.mtrl-sci2025
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…
cond-mat.mtrl-sci2025
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…