Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Aaron Gilad Kusne"'
Publikováno v:
Communications Materials, Vol 2, Iss 1, Pp 1-11 (2021)
Quantum materials host many exotic properties, which might be utilized for new electronic devices. Here, artificial intelligence for the discovery of quantum materials is discussed, covering both materials and property prediction, and high-throughput
Externí odkaz:
https://doaj.org/article/dd1255471d61448594b2032012542bc6
Autor:
Haotong Liang, Valentin Stanev, Aaron Gilad Kusne, Yuto Tsukahara, Kaito Ito, Ryota Takahashi, Mikk Lippmaa, Ichiro Takeuchi
Publikováno v:
Physical Review Materials. 6
Autonomous physical science is revolutionizing materials science. In these systems, machine learning (ML) controls experiment design, execution and analysis in a closed loop. Active learning, the ML field of optimal experiment design, selects each su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb769c772b859653dea0d1fbf1a11350
http://arxiv.org/abs/2204.05838
http://arxiv.org/abs/2204.05838
Publikováno v:
Communications Materials, Vol 2, Iss 1, Pp 1-11 (2021)
Artificial intelligence and machine learning are becoming indispensable tools in many areas of physics, including astrophysics, particle physics, and climate science. In the arena of quantum materials, the rise of new experimental and computational t
Autor:
Mariya Layurova, De Xin Chen, Tonio Buonassisi, Juan-Pablo Correa-Baena, Zekun Ren, Shijing Sun, Brian L. DeCost, Felipe Oviedo, Tofunmi Ogunfunmi, Savitha Ramasamy, Charles Settens, Ian Marius Peters, Aaron Gilad Kusne, Noor Titan Putri Hartono, Antonio M. Buscemi, Janak Thapa, Zhe Liu, Siyu I. P. Tian
Publikováno v:
Joule. 3:1437-1451
Summary Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique perovskite-inspired compositions within a 2-month period, with
Autor:
Aaron Gilad Kusne, Zachary T. Trautt, Martin L. Green, Eva M. Campo, Jason R. Hattrick-Simpers, Brian L. DeCost
Publikováno v:
Mach Learn Sci Technol
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, and chemistry communities. This perspective article identifies key scien
Autor:
J. B. Thayer, Kevin G. Yager, Sarah Cousineau, Daniel Ratner, Rama K. Vasudevan, Brian H. Toby, Yipeng Sun, Frank Alexander, Stuart Wilkins, Sergei V. Kalinin, Jay Jay Billings, R. Coffee, Daniela Ushizima, Subramanian K. R. S. Sankaranarayanan, Mark J. Stevens, Mariam Kiran, Apurva Mehta, Bobby G. Sumpter, Dean Hidas, Xiaobiao Huang, Peter Denes, M. C. Scott, Alex Hexemer, Anibal J. Ramirez-Cuesta, Aaron Gilad Kusne, Ian Foster, Mathieu Doucet
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3d198c187224ad2022c7aa1364d0b719
https://doi.org/10.2172/1630823
https://doi.org/10.2172/1630823
Autor:
Dipendra Jha, Aaron Gilad Kusne, Wei-keng Liao, Alok Choudhary, Nam Nguyen, Ankit Agrawal, Reda Al-Bahrani
Publikováno v:
IJCNN
X-ray diffraction (XRD) is a well-known technique used by scientists and engineers to determine the atomic-scale structures as a basis for understanding the composition-structure-property relationship of materials. The current approach for the analys
Autor:
Shijing Sun, Charles Settens, Zhe Liu, Siyu I. P. Tian, Noor Titan Putri Hartono, Felipe Oviedo, Brian L. DeCost, Giuseppe Romano, Tonio Buonassisi, Savitha Ramasamy, Aaron Gilad Kusne, Zekun Ren
Publikováno v:
npj Computational Materials, Vol 5, Iss 1, Pp 1-9 (2019)
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-enabled approach to predict crystallographic dimensionality and space gr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::35dc9c2e6e90d541f21e881c33549788
Autor:
J. Van Duren, John M. Gregoire, S. Empedocles, Jason R. Hattrick-Simpers, Joshua Martin, Kristin A. Persson, Martin L. Green, A. M. Joshi, Andriy Zakutayev, S. C. Barron, Ichiro Takeuchi, C. L. Choi, Eva M. Campo, T. Chiang, Aaron Gilad Kusne, Apurva Mehta, Zachary T. Trautt
The Materials Genome Initiative, a national effort to introduce new materials into the market faster and at lower cost, has made significant progress in computational simulation and modeling of materials. To build on this progress, a large amount of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::12b3666b5366358634a861be2e6de75b
https://resolver.caltech.edu/CaltechAUTHORS:20170616-160215339
https://resolver.caltech.edu/CaltechAUTHORS:20170616-160215339