Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Brian L. DeCost"'
Autor:
Brian L. DeCost, Elizabeth A. Holm
Publikováno v:
Data in Brief, Vol 9, Iss C, Pp 727-731 (2016)
This data article presents a data set comprised of 2048 synthetic scanning electron microscope (SEM) images of powder materials and descriptions of the corresponding 3D structures that they represent. These images were created using open source rende
Externí odkaz:
https://doaj.org/article/6a29f08f1e1044f78526499db49a1204
Autor:
Debra J. Audus, Kamal Choudhary, Brian L. DeCost, A. Gilad Kusne, Francesca Tavazza, James A. Warren
Publikováno v:
Artificial Intelligence for Science ISBN: 9789811265662
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::663f29bc190da6dc82027fc2a4014e01
https://doi.org/10.1142/9789811265679_0023
https://doi.org/10.1142/9789811265679_0023
Autor:
Brian L. DeCost, Elizabeth A. Holm
Publikováno v:
Data in Brief, Vol 16, Iss , Pp 1103- (2018)
Externí odkaz:
https://doaj.org/article/1ea4c84c24c840cc9ce4b8070a37b3eb
Autor:
Eli Rotenberg, Kristofer G. Reyes, Ian Foster, Tonio Buonassisi, Joseph Montoya, Keith A. Brown, Jason R. Hattrick-Simpers, Simon J. L. Billinge, Apurva Mehta, Chiwoo Park, Sylvia Smullin, Carla P. Gomes, A. Gilad Kusne, Brian L. DeCost, Semion K. Saikin, Eric A. Stach, Elsa Olivetti, John M. Gregoire, Joshua Schrier, Benji Maruyama, Valentin Stanev
Publikováno v:
Matter. 4:2702-2726
Summary Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8ee92c7a20ce076697535ef5a567900e
https://doi.org/10.1016/j.matt.2021.06.036
https://doi.org/10.1016/j.matt.2021.06.036
Autor:
Suchismita Sarker, Heshan Yu, A. Gilad Kusne, Corey Oses, Leonid A. Bendersky, Stefano Curtarolo, Ritesh Agarwal, Albert V. Davydov, Jason R. Hattrick-Simpers, Mo Li, Apurva Mehta, Changming Wu, Ichiro Takeuchi, Cormac Toher, Huairuo Zhang, Brian L. DeCost
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Nature Communications
Nature Communications
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 guide his discovery of celestial mechanics. In this work, we focus a cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0dbcc9504b767067491825d4de3c6fc7
https://doaj.org/article/04164405f2b441b89a1dbae389eb4d1c
https://doaj.org/article/04164405f2b441b89a1dbae389eb4d1c
Autor:
Houlong L. Zhuang, Vinit Sharma, Kamal Choudhary, Brian L. DeCost, Ruth Pachter, Andrew C. E. Reid, Albert V. Davydov, Evan J. Reed, Sergei V. Kalinin, Pinar Acar, Jason R. Hattrick-Simpers, Gowoon Cheon, Ankit Agrawal, David Vanderbilt, A. Gilad Kusne, Subhasish Mandal, Francesca Tavazza, Ghanshyam Pilania, Zachary T. Trautt, Kevin F. Garrity, Jie Jiang, Angela R. Hight Walker, Karin M. Rabe, Kristjan Haule, Andrea Centrone, Bobby G. Sumpter, Adam J. Biacchi, Xiaofeng Qian
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-13 (2020)
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) tech
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f4c82fa883525fe42fc620b5b4c9b8c
http://link.springer.com/article/10.1038/s41524-020-00440-1
http://link.springer.com/article/10.1038/s41524-020-00440-1
Autor:
Andriy Zakutayev, Caleb Phillips, Shijing Sun, Brian L. DeCost, Jason R. Hattrick-Simpers, Winnie Wong-Ng, A. Gilad Kusne, Howie Joress, Ichiro Takeuchi, Debra L. Kaiser, Heshan Yu, Janak Thapa, Tonio Buonassisi
Publikováno v:
Springer International Publishing
Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts, com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13012f7a4178c4df029b905e4cf06490
https://hdl.handle.net/1721.1/136823
https://hdl.handle.net/1721.1/136823
Publikováno v:
Molecular Systems Design & Engineering. 5:589-591
In this short comment we present a reproducibility study for our recent manuscript “A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials” by Hattrick-Simpers, et al., Mol. Syst. Des. Eng., 2018, 3
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d0d673efb019c6b676f2960978fb3936
https://doi.org/10.1039/c9me00138g
https://doi.org/10.1039/c9me00138g
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9bb745e343ef4ea3bc28e12c8712c8f5
https://doi.org/10.1016/j.joule.2019.05.014
https://doi.org/10.1016/j.joule.2019.05.014
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f37f4917800215a5fba2534e7849cb4c
https://pubmed.ncbi.nlm.nih.gov/33655211
https://pubmed.ncbi.nlm.nih.gov/33655211