Zobrazeno 1 - 10
of 54
pro vyhledávání: '"A. Gilad Kusne"'
Autor:
A. Gilad Kusne, Heshan Yu, Changming Wu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Suchismita Sarker, Corey Oses, Cormac Toher, Stefano Curtarolo, Albert V. Davydov, Ritesh Agarwal, Leonid A. Bendersky, Mo Li, Apurva Mehta, Ichiro Takeuchi
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new
Externí odkaz:
https://doaj.org/article/04164405f2b441b89a1dbae389eb4d1c
Autor:
A. Gilad Kusne, Austin McDannald, Brian DeCost, Corey Oses, Cormac Toher, Stefano Curtarolo, Apurva Mehta, Ichiro Takeuchi
Publikováno v:
Frontiers in Physics, Vol 10 (2022)
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has
Externí odkaz:
https://doaj.org/article/8cc99020ad8b4a0b9c2303f1b463ade6
Autor:
Sean W. Fackler, Vasileios Alexandrakis, Dennis König, A. Gilad Kusne, Tieren Gao, Matthew J. Kramer, Drew Stasak, Kenny Lopez, Brad Zayac, Apurva Mehta, Alfred Ludwig, Ichiro Takeuchi
Publikováno v:
Science and Technology of Advanced Materials, Vol 18, Iss 1, Pp 231-238 (2017)
Thin film libraries of Fe-Co-V were fabricated by combinatorial sputtering to study magnetic and structural properties over wide ranges of composition and thickness by high-throughput methods: synchrotron X-ray diffraction, magnetometry, composition,
Externí odkaz:
https://doaj.org/article/5ee7cd0cf87546b89fd57e068fe54da3
Publikováno v:
npj Computational Materials, Vol 3, Iss 1, Pp 1-9 (2017)
Machine learning: Spying enhanced materials with x-ray vision Using algorithms to automatically spot variations in massive X-ray diffraction data sets may improve design of multi-component alloys. Having three or more metals in an alloy can lead to o
Externí odkaz:
https://doaj.org/article/64f79ca4c771418085a08eb761244bfb
Publikováno v:
IEEE Access, Vol 5, Pp 20524-20535 (2017)
Today's cities generate tremendous amounts of data, thanks to a boom in affordable smart devices and sensors. The resulting big data creates opportunities to develop diverse sets of context-aware services and systems, ensuring smart city services are
Externí odkaz:
https://doaj.org/article/066249251d734c4e83545eaf9153ec5d
Publikováno v:
APL Materials, Vol 4, Iss 5, Pp 053211-053211-11 (2016)
With their ability to rapidly elucidate composition-structure-property relationships, high-throughput experimental studies have revolutionized how materials are discovered, optimized, and commercialized. It is now possible to synthesize and character
Externí odkaz:
https://doaj.org/article/f4e13e0aca654b1eaf8e992bacbcd689
Autor:
Logan Saar, Haotong Liang, Alex Wang, Austin McDannald, Efrain Rodriguez, Ichiro Takeuchi, A. Gilad Kusne
Publikováno v:
MRS Bulletin. 47:881-885
Publikováno v:
Journal of Applied Crystallography. 55:882-889
The structural solution problem can be a daunting and time-consuming task. Especially in the presence of impurity phases, current methods, such as indexing, become more unstable. In this work, the novel approach of semi-supervised learning is applied
Autor:
Arun Mannodi-Kanakkithodi, Austin McDannald, Shijing Sun, Saaketh Desai, Keith A. Brown, A. Gilad Kusne
Publikováno v:
MRS Bulletin.
Autor:
A. Gilad Kusne, Austin McDannald
Publikováno v:
Matter.