47 Material informatics

2020 ◽  
pp. 235-236
Keyword(s):  
APL Materials ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 119902
Author(s):  
Zong-Li Liu ◽  
Peng Kang ◽  
Yu Zhu ◽  
Lei Liu ◽  
Hong Guo

2021 ◽  
Author(s):  
Prajeesha . ◽  
Mohit N Bagur ◽  
Pranav Sankar M ◽  
Amrita Ramesh ◽  
Siddhanth Srikanth

APL Materials ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 061104 ◽  
Author(s):  
Zhong-Li Liu ◽  
Peng Kang ◽  
Yu Zhu ◽  
Lei Liu ◽  
Hong Guo

2021 ◽  
pp. 102960
Author(s):  
He Huang ◽  
Xin Wang ◽  
Jie Shi ◽  
Huogen Huang ◽  
Yawen Zhao ◽  
...  

2018 ◽  
Vol 11 ◽  
pp. 1-5 ◽  
Author(s):  
Edward Swinnich ◽  
Yash Jayeshbhai Dave ◽  
E. Bruce Pitman ◽  
Scott Broderick ◽  
Baishakhi Mazumder ◽  
...  

Author(s):  
Ryan Murdock ◽  
Steven Kauwe ◽  
Anthony Wang ◽  
Taylor Sparks

<div>New methods for describing materials as vectors in order to predict their properties using machine learning are common in the field of material informatics. However, little is known about the comparative efficacy of these methods. This work sets out to make clear which featurization methods should be used across various circumstances. Our findings include, surprisingly, that simple one-hot encoding of elements can be as effective as traditional and new descriptors when using large amounts of data. However, in the absence of large datasets or data that is not fully representative we show that domain knowledge offers advantages in predictive ability.</div><div><br></div>


Author(s):  
Ryan Murdock ◽  
Steven Kauwe ◽  
Anthony Wang ◽  
Taylor Sparks

<div>New methods for describing materials as vectors in order to predict their properties using machine learning are common in the field of material informatics. However, little is known about the comparative efficacy of these methods. This work sets out to make clear which featurization methods should be used across various circumstances. Our findings include, surprisingly, that simple one-hot encoding of elements can be as effective as traditional and new descriptors when using large amounts of data. However, in the absence of large datasets or data that is not fully representative we show that domain knowledge offers advantages in predictive ability.</div><div><br></div>


2021 ◽  
Vol 46 (4) ◽  
pp. 888
Author(s):  
Wang Xi ◽  
Yida Liu ◽  
Jinlin Song ◽  
Run Hu ◽  
Xiaobing Luo

2016 ◽  
Vol 6 ◽  
pp. 9-16 ◽  
Author(s):  
Taylor Moot ◽  
Olexandr Isayev ◽  
Robert W. Call ◽  
Shannon M. McCullough ◽  
Morgan Zemaitis ◽  
...  

2020 ◽  
Vol 05 (02) ◽  
pp. 2040002
Author(s):  
Ying Zhou ◽  
Guoyou Gan ◽  
Jianhong Yi ◽  
Yumin Lai ◽  
Yingwu Wang ◽  
...  

The core philosophy of Materials Genome Initiative (MGI) is the transition of the way of new materials design from the traditional “trial-and-error” approach to the in-silico materials design approach which employs intensive computing and material informatics. In June 2011, President Barack Obama launched MGI alongside the Advanced Manufacturing Partnership to help businesses discover, develop and deploy new materials twice as fast. In this paper, the concept of rare and precious genome is presented first, followed by the progress of MGI. After that, we focus on the research status of the rare and precious metals’ MGI including the computational tools, the high-throughput experimental methodologies and the rare and precious metals database. We also introduce the application of MGI in the development of rare and precious metal materials, outline the remaining fundamental challenges and present an outlook on the future of the rare and precious metals’ MGI.


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