Accelerated Materials Design of Lithium Superionic Conductors Based on First‐Principles Calculations and Machine Learning Algorithms. Issue 8 (5th April 2013)

• Record Type: Journal Article
• Authors: Fujimura, Koji; Seko, Atsuto; Koyama, Yukinori; Kuwabara, Akihide; Kishida, Ippei; Shitara, Kazuki; Fisher, Craig A. J.; Moriwake, Hiroki; Tanaka, Isao
• Is Part Of: Advanced energy materials. Volume 3:Issue 8(2013:Aug.)
• Page Start: 980
• Page End: 985
• Publication Date: 2013-04-05
• Subjects: Energy harvesting -- Materials -- Periodicals; Energy conversion -- Materials -- Periodicals; Energy storage -- Materials -- Periodicals; Photovoltaics -- Periodicals; Fuel cells -- Periodicals; Thermoelectric materials -- Periodicals; 621.31
• DOI: 10.1002/aenm.201300060
• Languages: English
• ISSNs: 1614-6832

Abstract

<abstract abstract-type="graphical" xml:lang="en"> <title> <x xml:space="preserve">Abstract</x> </title> <p> <bold>A method for efficiently screening a wide compositional and structural phase space of LISICON‐type superionic conductors</bold> 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. <boxed-text content-type="graphic" position="anchor" orientation="portrait"><graphic position="anchor" mimetype="image" xlink:href="ark:/27927/pgg36h3q8tc" orientation="portrait" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink" /></boxed-text></p> </abstract>