5 citations · 11 across the 10 of their papers we have counts for
3 papers · 1 filter
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
Felipe Oviedo, Zekun Ren, Shijing Sun +9
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-en…
High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel
Brian L. DeCost, Bo Lei, Toby Francis +1
We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated m…
Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
Kamal Choudhary, Brian DeCost, Francesca Tavazza
We present a complete set of chemo-structural descriptors to significantly extend the applicability of machine-learning (ML) in material screening and mapping energy landscape for…