Drawing a materials map with an autoencoder for lithium ionic conductors
Abstract Efforts to optimize known materials and enhance their performance are ongoing, driven by the advancements resulting from the discovery of novel functional materials. Traditionally, the search for and optimization of functional materials has relied on the experience and intuition of speciali...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-43921-1 |
| _version_ | 1827634585067847680 |
|---|---|
| author | Yudai Yamaguchi Taruto Atsumi Kenta Kanamori Naoto Tanibata Hayami Takeda Masanobu Nakayama Masayuki Karasuyama Ichiro Takeuchi |
| author_facet | Yudai Yamaguchi Taruto Atsumi Kenta Kanamori Naoto Tanibata Hayami Takeda Masanobu Nakayama Masayuki Karasuyama Ichiro Takeuchi |
| author_sort | Yudai Yamaguchi |
| collection | DOAJ |
| description | Abstract Efforts to optimize known materials and enhance their performance are ongoing, driven by the advancements resulting from the discovery of novel functional materials. Traditionally, the search for and optimization of functional materials has relied on the experience and intuition of specialized researchers. However, materials informatics (MI), which integrates materials data and machine learning, has frequently been used to realize systematic and efficient materials exploration without depending on manual tasks. Nonetheless, the discovery of new materials using MI remains challenging. In this study, we propose a method for the discovery of materials outside the scope of existing databases by combining MI with the experience and intuition of researchers. Specifically, we designed a two-dimensional map that plots known materials data based on their composition and structure, facilitating researchers’ intuitive search for new materials. The materials map was implemented using an autoencoder-based neural network. We focused on the conductivity of 708 lithium oxide materials and considered the correlation with migration energy (ME), an index of lithium-ion conductivity. The distribution of existing data reflected in the materials map can contribute to the development of new lithium-ion conductive materials by enhancing the experience and intuition of material researchers. |
| first_indexed | 2024-03-09T15:16:22Z |
| format | Article |
| id | doaj.art-315105070f71445faa9ded27022dfdca |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:16:22Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-315105070f71445faa9ded27022dfdca2023-11-26T13:03:30ZengNature PortfolioScientific Reports2045-23222023-10-0113111110.1038/s41598-023-43921-1Drawing a materials map with an autoencoder for lithium ionic conductorsYudai Yamaguchi0Taruto Atsumi1Kenta Kanamori2Naoto Tanibata3Hayami Takeda4Masanobu Nakayama5Masayuki Karasuyama6Ichiro Takeuchi7Department of Advanced Ceramics, Nagoya Institute of TechnologyDepartment of Advanced Ceramics, Nagoya Institute of TechnologyDepartment of Computer Science, Nagoya Institute of TechnologyDepartment of Advanced Ceramics, Nagoya Institute of TechnologyDepartment of Advanced Ceramics, Nagoya Institute of TechnologyDepartment of Advanced Ceramics, Nagoya Institute of TechnologyDepartment of Computer Science, Nagoya Institute of TechnologyDepartment of Computer Science, Nagoya Institute of TechnologyAbstract Efforts to optimize known materials and enhance their performance are ongoing, driven by the advancements resulting from the discovery of novel functional materials. Traditionally, the search for and optimization of functional materials has relied on the experience and intuition of specialized researchers. However, materials informatics (MI), which integrates materials data and machine learning, has frequently been used to realize systematic and efficient materials exploration without depending on manual tasks. Nonetheless, the discovery of new materials using MI remains challenging. In this study, we propose a method for the discovery of materials outside the scope of existing databases by combining MI with the experience and intuition of researchers. Specifically, we designed a two-dimensional map that plots known materials data based on their composition and structure, facilitating researchers’ intuitive search for new materials. The materials map was implemented using an autoencoder-based neural network. We focused on the conductivity of 708 lithium oxide materials and considered the correlation with migration energy (ME), an index of lithium-ion conductivity. The distribution of existing data reflected in the materials map can contribute to the development of new lithium-ion conductive materials by enhancing the experience and intuition of material researchers.https://doi.org/10.1038/s41598-023-43921-1 |
| spellingShingle | Yudai Yamaguchi Taruto Atsumi Kenta Kanamori Naoto Tanibata Hayami Takeda Masanobu Nakayama Masayuki Karasuyama Ichiro Takeuchi Drawing a materials map with an autoencoder for lithium ionic conductors Scientific Reports |
| title | Drawing a materials map with an autoencoder for lithium ionic conductors |
| title_full | Drawing a materials map with an autoencoder for lithium ionic conductors |
| title_fullStr | Drawing a materials map with an autoencoder for lithium ionic conductors |
| title_full_unstemmed | Drawing a materials map with an autoencoder for lithium ionic conductors |
| title_short | Drawing a materials map with an autoencoder for lithium ionic conductors |
| title_sort | drawing a materials map with an autoencoder for lithium ionic conductors |
| url | https://doi.org/10.1038/s41598-023-43921-1 |
| work_keys_str_mv | AT yudaiyamaguchi drawingamaterialsmapwithanautoencoderforlithiumionicconductors AT tarutoatsumi drawingamaterialsmapwithanautoencoderforlithiumionicconductors AT kentakanamori drawingamaterialsmapwithanautoencoderforlithiumionicconductors AT naototanibata drawingamaterialsmapwithanautoencoderforlithiumionicconductors AT hayamitakeda drawingamaterialsmapwithanautoencoderforlithiumionicconductors AT masanobunakayama drawingamaterialsmapwithanautoencoderforlithiumionicconductors AT masayukikarasuyama drawingamaterialsmapwithanautoencoderforlithiumionicconductors AT ichirotakeuchi drawingamaterialsmapwithanautoencoderforlithiumionicconductors |