A New Class of Superionic Solid-State Lithium-Ion Conductors: Lithium-Phosphido Silicates, Germanates, and Aluminates

2020 ◽  
Vol MA2020-02 (5) ◽  
pp. 968-968
Author(s):  
Thomas Friedrich Fässler
2013 ◽  
Vol 15 (16) ◽  
pp. 6107 ◽  
Author(s):  
Fabio Rosciano ◽  
Paolo P. Pescarmona ◽  
Kristof Houthoofd ◽  
Andre Persoons ◽  
Patrick Bottke ◽  
...  

1990 ◽  
Vol 210 ◽  
Author(s):  
Michael D. Lewis

AbstractThe search for lithium ion conductors in crystalline or polycrystalline solid state ionic materials has resulted in finding a new class of compounds. Five new, stable, homogeneous compounds formed in the lithium alcohol systems, LiI—(ROH)x for R=CH3CH2OH, CH3CH2CH2OH, CH3CHOHCH3,, and CH3OH have been identified and characterized.


2019 ◽  
Vol 9 (21) ◽  
pp. 1900807 ◽  
Author(s):  
William Fitzhugh ◽  
Fan Wu ◽  
Luhan Ye ◽  
Wenye Deng ◽  
Pengfei Qi ◽  
...  

2015 ◽  
Vol 278 ◽  
pp. 268-274 ◽  
Author(s):  
Jacilynn A. Brant ◽  
Kasey P. Devlin ◽  
Christian Bischoff ◽  
Deborah Watson ◽  
Steve W. Martin ◽  
...  

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Ying Zhang ◽  
Xingfeng He ◽  
Zhiqian Chen ◽  
Qiang Bai ◽  
Adelaide M. Nolan ◽  
...  

AbstractAlthough machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning, which does not require labeled data and thus alleviates the data scarcity challenge. Using solid-state Li-ion conductors as a model problem, unsupervised materials discovery utilizes a limited quantity of conductivity data to prioritize a candidate list from a wide range of Li-containing materials for further accurate screening. Our unsupervised learning scheme discovers 16 new fast Li-conductors with conductivities of 10−4–10−1 S cm−1 predicted in ab initio molecular dynamics simulations. These compounds have structures and chemistries distinct to known systems, demonstrating the capability of unsupervised learning for discovering materials over a wide materials space with limited property data.


2017 ◽  
Vol 173 ◽  
pp. 64-70 ◽  
Author(s):  
Tsukasa Hirayama ◽  
Yuka Aizawa ◽  
Kazuo Yamamoto ◽  
Takeshi Sato ◽  
Hidekazu Murata ◽  
...  

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