| Project/Area Number |
21K14729
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 36020:Energy-related chemistry
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| Research Institution | National Institute for Materials Science |
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Principal Investigator |
JALEM Randy 国立研究開発法人物質・材料研究機構, エネルギー・環境材料研究センター, 主任研究員 (20767553)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2023: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2022: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2021: ¥260,000 (Direct Cost: ¥200,000、Indirect Cost: ¥60,000)
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| Keywords | solid-state batteries / solid electrolytes / machine learning / dft calculations / molecular dynamics / data science / ion dynamics / computational science / 機械学習ポテンシャル / 固体電解質 / 全固体電池 / 第一原理計算 |
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| Outline of Research at the Start |
This proposal seeks to develop robust machine-learning potentials based on group-theoretical high-order rotational invariants for use in the computational design of solid electrolytes for all-solid-state batteries.
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| Outline of Final Research Achievements |
In this work, a machine learning potential based on moment tensor potential (MTP) approach was developed for use in the design and property evaluation of solid electrolytes for all solid-state batteries. The target solid electrolyte is beta-Li3PS4 and MTP development was performed to study the bulk and grain boundary Li dynamics of the material.
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| Academic Significance and Societal Importance of the Research Achievements |
beta-Li3PS4は現在商業的に利用可能な固体電解質材料の一つであり、その性能向上は全固体電池技術の進展に貢献する可能性がある。開発された機械学習ポテンシャルは、beta-Li3PS4固体電解質のリチウムイオン伝導性に影響を与えるより複雑な構造や粒子形態特性を調査するために使用できる。これには転位や孔領域を含む界面などが含まれる。
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