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Accelerated Materials Design of Lithium Superionic Conductors Based on First‐Principles Calculations and Machine Learning Algorithms
Author(s) -
Fujimura Koji,
Seko Atsuto,
Koyama Yukinori,
Kuwabara Akihide,
Kishida Ippei,
Shitara Kazuki,
Fisher Craig A. J.,
Moriwake Hiroki,
Tanaka Isao
Publication year - 2013
Publication title -
advanced energy materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.08
H-Index - 220
eISSN - 1614-6840
pISSN - 1614-6832
DOI - 10.1002/aenm.201300060
Subject(s) - fast ion conductor , lithium (medication) , materials science , electrical conductor , process (computing) , algorithm , space (punctuation) , computer science , chemistry , electrolyte , electrode , medicine , composite material , endocrinology , operating system
A method for efficiently screening a wide compositional and structural phase space of LISICON‐type superionic conductors is presented that utilizes a machine‐learning technique for combining theoretical and experimental datasets. By iteratively performing systematic sets of first‐principles calculations and focused experiments, it is shown how the materials design process can be greatly accelerated, suggesting potentially superior candidate lithium superionic conductors.