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固态存材料中的机器学习:挑战和前景

Panpan Zhou1,2, Qianwen Zhou2, Xuezhang Xiao2,3

  • 1College of Materials Science and Engineering, Hohai University, Changzhou, Jiangsu, 213200, China.

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PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 加快了高性能固态存材料 (HSM) 的发现. 本综述详细介绍了ML的应用,挑战以及可持续能源存储解决方案的路线图.

关键词:
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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
  • 储能 储能 储能 储能 储能 储能

背景情况:

  • 高性能固态存材料 (HSM) 对可持续能源至关重要.
  • 机器学习 (ML) 提供了强大的工具来克服HSM研究的局限性.
  • 关键的挑战包括储存能力低和困难的循环条件.

研究的目的:

  • 审查固态储存中ML应用的现状.
  • 识别挑战,并提出解决方案在HSM研究中的ML.
  • 为 ML 驱动的 HSM 开发制定未来的路线图.

主要方法:

  • 对HSM的ML模型和数据集的文献综述.
  • 分析各种HSM类 (例如基于Ti,基于Mg) 的ML应用.
  • 对数据质量,可用性和模型可解释性的批判性评估.

主要成果:

  • ML成功地解决了诸如低容量和HSM中的循环条件等问题.
  • 证明了ML在利用构成-结构-属性关系中的作用.
  • 通过高通量选优化为燃料电池应用的基于Ti的HSM.

结论:

  • 机器学习是设计新型HSM的关键工具.
  • 解决数据的局限性和模型的可解释性对于在这个领域推进ML至关重要.
  • 一个战略路线图可以增强ML对高效和可持续储存的影响.