6 papers
On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning
A. Gilad Kusne, Heshan Yu, Changming Wu +13
Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to gu…
Scientific AI in materials science: a path to a sustainable and scalable paradigm
Brian DeCost, Jason Hattrick-Simpers, Zachary Trautt +3
Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, an…
A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al
Howie Joress, Brian L. DeCost, Suchismita Sarker +7
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…
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…