数据驱动的晶体和玻璃状固态电解质的原子模型
1Department of Materials Science and Engineering, Iowa State University, Ames, Iowa 50011, USA. qan@iastate.edu.
概括
机器学习力场 (ML-FFs) 加快了对固体电解质的研究,以获得更安全,更高能量的电池. 这些先进的模型为开发下一代全固态电池技术提供了至关重要的原子洞察力.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 电化学 电化学 电化学
背景情况:
- 全固态电池提供更高的安全性和能量密度,但需要对固体电解质有更深入的了解.
- 原子模拟是这种理解的关键,但传统方法在计算上昂贵.
研究的目的:
- 审查用于固体电解质研究的机器学习力场 (ML-FFs) 的最新进展.
- 突出ML-FFs在研究晶体和玻璃状固体电解质中的应用.
- 讨论ML-FF在电池开发中的挑战和未来方向.
主要方法:
- 讨论ML-FF框架和培训策略.
- 不同ML-FF模型的比较,包括可转移性和不确定性量化.
- 在ML-FF开发中对数据生成和验证的最佳实践的审查.
主要成果:
- ML-FFs能够进行大规模,长时间的模拟,超越了ab initio方法.
- 应用程序揭示了对离子运输,缺陷,结构-属性关系,相位稳定性和接口现象的洞察.
- ML-FF 已被证明对晶体和玻璃状固体电解质都有效.
结论:
- ML-FF是加速发现和优化固体电解质的强大工具.
- 主要挑战仍然存在,包括远程静电学,化学反应性和多组件系统.
- 对于实际的全固态电池来说,ML-FF的进一步开发将至关重要.
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