人工智能驱动的大数据框架用于电池中的电极-电解质介面
Abdullah Bin Faheem1, Zengyu Han2, Dongshuang Wu2
1School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore.
Advanced materials (Deerfield Beach, Fla.)
|January 10, 2026
概括
人工智能 (AI) 和大数据正在通过改变电极-电解质界面 (EEI) 来彻底改变可充电电池设计. 这些策略使得以数据驱动的方式发现先进的电池材料,以提高性能和寿命.
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
- 数据科学数据科学数据科学
背景情况:
- 电极-电解质界面 (EEI) 对于可充电电池的性能和寿命至关重要.
- 理解和设计EEI是复杂的,需要先进的计算和实验方法.
研究的目的:
- 提供关于人工智能和大数据战略的全面视角,以改变EEI的理解和设计.
- 突出这些策略在提高电池性能和寿命方面的关键作用.
主要方法:
- 结合高吞吐量实验和高吞吐量计算 (HTC) 来实现多样化的数据集生成.
- 利用人工智能编排的工作流程和机器学习模型进行数据分析和预测.
- 整合分子层次的EEI理解与宏观设备性能.
主要成果:
- 人工智能和大数据可以揭示接口过程的机械基础.
- 实现了相间行为预测和数据驱动的最佳材料组合的发现.
- 智能,以数据为中心的框架促进了下一代电池系统的理性工程.
结论:
- 人工智能和大数据为可充电电池开发提供了变革性的潜力.
- 解决数据标准化和互操作性方面的挑战对于未来的进步至关重要.
- 智能框架是通过合理设计推进电池技术的关键.
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