Unsupervised discovery of solid-state lithium ion conductors
Predictions of new solid-state Li-ion conductors are challenging due to the diverse chemistries and compositions involved. Here the authors combine unsupervised learning techniques and molecular dynamics simulations to discover new compounds with high Li-ion conductivity.
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2019-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-13214-1 |
| _version_ | 1818991443314737152 |
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| author | Ying Zhang Xingfeng He Zhiqian Chen Qiang Bai Adelaide M. Nolan Charles A. Roberts Debasish Banerjee Tomoya Matsunaga Yifei Mo Chen Ling |
| author_facet | Ying Zhang Xingfeng He Zhiqian Chen Qiang Bai Adelaide M. Nolan Charles A. Roberts Debasish Banerjee Tomoya Matsunaga Yifei Mo Chen Ling |
| author_sort | Ying Zhang |
| collection | DOAJ |
| description | Predictions of new solid-state Li-ion conductors are challenging due to the diverse chemistries and compositions involved. Here the authors combine unsupervised learning techniques and molecular dynamics simulations to discover new compounds with high Li-ion conductivity. |
| first_indexed | 2024-12-20T20:10:21Z |
| format | Article |
| id | doaj.art-c1c724a0a205479583f3afacc34a107c |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-20T20:10:21Z |
| publishDate | 2019-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-c1c724a0a205479583f3afacc34a107c2022-12-21T19:27:50ZengNature PortfolioNature Communications2041-17232019-11-011011710.1038/s41467-019-13214-1Unsupervised discovery of solid-state lithium ion conductorsYing Zhang0Xingfeng He1Zhiqian Chen2Qiang Bai3Adelaide M. Nolan4Charles A. Roberts5Debasish Banerjee6Tomoya Matsunaga7Yifei Mo8Chen Ling9Toyota Research Institute of North AmericaDepartment of Materials Science and Engineering, University of MarylandDepartment of Computer Science, Virginia TechDepartment of Materials Science and Engineering, University of MarylandDepartment of Materials Science and Engineering, University of MarylandToyota Research Institute of North AmericaToyota Research Institute of North AmericaToyota Research Institute of North AmericaDepartment of Materials Science and Engineering, University of MarylandToyota Research Institute of North AmericaPredictions of new solid-state Li-ion conductors are challenging due to the diverse chemistries and compositions involved. Here the authors combine unsupervised learning techniques and molecular dynamics simulations to discover new compounds with high Li-ion conductivity.https://doi.org/10.1038/s41467-019-13214-1 |
| spellingShingle | Ying Zhang Xingfeng He Zhiqian Chen Qiang Bai Adelaide M. Nolan Charles A. Roberts Debasish Banerjee Tomoya Matsunaga Yifei Mo Chen Ling Unsupervised discovery of solid-state lithium ion conductors Nature Communications |
| title | Unsupervised discovery of solid-state lithium ion conductors |
| title_full | Unsupervised discovery of solid-state lithium ion conductors |
| title_fullStr | Unsupervised discovery of solid-state lithium ion conductors |
| title_full_unstemmed | Unsupervised discovery of solid-state lithium ion conductors |
| title_short | Unsupervised discovery of solid-state lithium ion conductors |
| title_sort | unsupervised discovery of solid state lithium ion conductors |
| url | https://doi.org/10.1038/s41467-019-13214-1 |
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