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A. Gilad Kusne
Researcher at National Institute of Standards and Technology
Publications - 30
Citations - 1216
A. Gilad Kusne is an academic researcher from National Institute of Standards and Technology. The author has contributed to research in topics: Computer science & Bravais lattice. The author has an hindex of 9, co-authored 16 publications receiving 605 citations. Previous affiliations of A. Gilad Kusne include University of Maryland, College Park.
Papers
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Journal ArticleDOI
Machine learning modeling of superconducting critical temperature
Valentin Stanev,Corey Oses,A. Gilad Kusne,A. Gilad Kusne,Efrain E. Rodriguez,Johnpierre Paglione,Stefano Curtarolo,Stefano Curtarolo,Ichiro Takeuchi +8 more
TL;DR: In this article, several machine learning schemes are developed to model the critical temperatures (Tc) of the 12,000+ known superconductors available via the SuperCon database.
Journal ArticleDOI
On-the-fly closed-loop materials discovery via Bayesian active learning.
A. Gilad Kusne,A. Gilad Kusne,Heshan Yu,Changming Wu,Huairuo Zhang,Jason R. Hattrick-Simpers,Brian L. DeCost,Suchismita Sarker,Corey Oses,Cormac Toher,Stefano Curtarolo,Albert V. Davydov,Ritesh Agarwal,Leonid A. Bendersky,Mo Li,Apurva Mehta,Ichiro Takeuchi +16 more
TL;DR: An autonomous materials discovery methodology for functional inorganic compounds is demonstrated which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools.
Journal Article
Machine learning modeling of superconducting critical temperature
Valentin Stanev,Corey Oses,A. Gilad Kusne,Efrain E. Rodriguez,Johnpierre Paglione,Stefano Curtarolo,Ichiro Takeuchi +6 more
TL;DR: In this article, several machine learning schemes are developed to model the critical temperatures (Tc) of the 12,000+ known superconductors available via the SuperCon database.
Journal ArticleDOI
Autonomous experimentation systems for materials development: A community perspective
Eric A. Stach,Brian L. DeCost,A. Gilad Kusne,A. Gilad Kusne,Jason R. Hattrick-Simpers,Keith A. Brown,Kristofer G. Reyes,Joshua Schrier,Simon J. L. Billinge,Simon J. L. Billinge,Tonio Buonassisi,Ian Foster,Ian Foster,Carla P. Gomes,John M. Gregoire,Apurva Mehta,Joseph Montoya,Elsa Olivetti,Chiwoo Park,Eli Rotenberg,Semion K. Saikin,Sylvia Smullin,Valentin Stanev,Benji Maruyama +23 more
TL;DR: In this paper, the authors discuss the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm and outline the current status, barriers and needed investments, culminating with a vision for the path forward.
Journal ArticleDOI
Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge
TL;DR: In this paper, the authors review the field of automated phase diagram attribution and discuss the impact that emerging computational approaches will have in the generation of phase diagrams and beyond, as well as the impact of computational approaches on phase diagram generation.