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机器学习驱动的高通量选用于高能量密度和稳定的NASICON阴极.

Jinyoung Jeong1, Juo Kim1, Jiwon Sun1

  • 1School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea.

ACS applied materials & interfaces
|May 2, 2024
PubMed
概括

研究人员使用密度函数理论和机器学习开发了一个选平台,以发现理想的离子电池阴极材料. 这加快了对具有高压和稳定的NASICON结构有前途的识别速度.

科学领域:

  • 材料科学 材料科学 材料科学
  • 电化学 电化学 电化学
  • 计算化学计算化学

背景情况:

  • 离子电池 (SIB) 在特定能量和体积扭曲方面存在局限性.
  • 超离子导体 (NASICON) 材料为SIBs提供结构稳定性和高操作电压.

研究的目的:

  • 开发一个计算选平台,用于发现新的纳西康正极材料.
  • 为SIB应用确定具有增强电化学性能和稳定性的NASICON结构.

主要方法:

  • 使用密度函数理论 (DFT) 计算和机器学习 (ML) 进行高通量选.
  • 从现有的电极数据生成了一个训练数据库,并构建了一个测试组,由3126个NASICON结构与各种dopants.
  • 采用ML替代模型和ML原子间潜力 (MLIP) 来进行材料性质预测和稳定性评估.

主要成果:

  • 选了3126种潜在的NASICON结构,确定了796种材料,满足了形成能量,船体上方能量,体积变化和理论容量的标准.
  • 使用MLIP选择了热力学稳定的化纳西康配置.
  • 经过DFT计算,确定了50种NASICON候选材料,平均电压≥3.5V.

结论:

关键词:
纳西康 (NASICON) 的阴极.离子电池 离子电池密度函数理论密度函数理论兴奋剂选对象的选机器学习是机器学习.

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  • 开发的基于DFT和ML的平台有效地加速了为SIBs发现最佳NASICON阴极材料的发现.
  • 确定了50种候选材料,这些材料代表了下一代离子电池开发的有希望的途径.
  • 这种方法大大减少了探索先进电池材料所需的资源.