Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms

被引：211

作者：

Fujimura, Koji ^{[1
,2
]}

Seko, Atsuto ^{[1
]}

Koyama, Yukinori ^{[1
]}

Kuwabara, Akihide ^{[2
]}

Kishida, Ippei ^{[3
]}

Shitara, Kazuki ^{[1
]}

Fisher, Craig A. J. ^{[2
]}

Moriwake, Hiroki ^{[2
]}

Tanaka, Isao ^{[1
,2
]}

机构：

[1] Kyoto Univ, Dept Mat Sci & Engn, Sakyo Ku, Kyoto 6068501, Japan

[2] Japan Fine Ceram Ctr, Nanostruct Res Lab, Atsuta Ku, Nagoya, Aichi 4568587, Japan

[3] Osaka City Univ, Dept Mech Engn, Sumiyoshi Ku, Osaka 5588585, Japan

来源：

ADVANCED ENERGY MATERIALS | 2013年 / 3卷 / 08期

关键词：

cluster expansion; first-principles molecular dynamics; LISICON; solid electrolyte; support-vector regression; LISICON SOLID-SOLUTIONS; IONIC-CONDUCTIVITY; AB-INITIO; CRYSTAL-STRUCTURE; DIFFUSION MECHANISMS; MOLECULAR-DYNAMICS; MIGRATION; LI14ZN(GEO4)4; ENERGY; LI;

D O I：

10.1002/aenm.201300060

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

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. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

引用

页码：980 / 985

页数：6

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