Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms
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 calculat...
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| Veröffentlicht in: | Advanced energy materials 2013-08, Vol.3 (8), p.980-985 |
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| container_end_page | 985 |
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| container_issue | 8 |
| container_start_page | 980 |
| container_title | Advanced energy materials |
| container_volume | 3 |
| creator | Fujimura, Koji Seko, Atsuto Koyama, Yukinori Kuwabara, Akihide Kishida, Ippei Shitara, Kazuki Fisher, Craig A. J. Moriwake, Hiroki Tanaka, Isao |
| description | 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. |
| doi_str_mv | 10.1002/aenm.201300060 |
| format | Article |
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| language | eng |
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| source | Wiley Online Library All Journals |
| subjects | cluster expansion Clusters Conductors Design engineering first-principles molecular dynamics LISICON Lithium Mathematical analysis Regression Screening solid electrolyte Solid electrolytes support-vector regression |
| title | Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms |
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