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Towards automated design of corrosion resistant alloy coatings with an autonomous scanning droplet cell
Brian DeCost, Howie Joress, Suchismita Sarker +2
We present an autonomous scanning droplet cell platform designed for on-demand alloy electrodeposition and real-time electrochemical characterization for investigating the corrosio…
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…
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…