Publications

Displaying 26 - 28 of 28

{A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al

June 4, 2020

Author(s)

Howard L. Joress, Brian L. DeCost, Suchismita Sarker, Trevor M. Braun, Logan T. Ward, Kevin Laws, Apurva Mehta, Jason R. Hattrick-Simpers

Based on a set of machine learning predictions of glass formation in the Ni-Ti-Al system, we have undertaken a high-throughput experimental study of that system. We utilized rapid synthesis followed by high- throughput structural and electrochemical

High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel

February 1, 2019

Author(s)

Brian DeCost, Toby Francis, Elizabeth A. Holm

We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively. We explore two microstructure segmentation tasks

Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape

August 3, 2018

Author(s)

Kamal Choudhary, Brian L. DeCost, Francesca M. Tavazza

We present a complete set of chemo-structural descriptors to significantly extend the applicability of machine learning (ML) in material screening and mapping the energy landscape for multicomponent systems. These descriptors allow differentiating between

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