桥梁尺度和范式:从原子模拟到人工智能增强模拟离子电容器的视角
Huiyang Fan1, Lei Li2, Huihong Wang3
1State Key Laboratory of Clean Energy Utilization, College of Energy Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China. bozh@zju.edu.cn.
Physical chemistry chemical physics : PCCP
|November 26, 2025
概括
离子电容器 (SIC) 提供具有成本效益的能量存储. 先进的模拟对于理解SIC机制和优化材料以获得更好的性能至关重要.
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
- 计算化学的计算化学
背景情况:
- 离子电容器 (SIC) 由于大量的低成本,因此对大规模储能充满希望.
- 了解离子储存,电解质溶解和界面行为是提高SIC性能的关键.
- 目前的局限性阻碍了SIC的实际应用,尽管它们具有潜力.
研究的目的:
- 为了解离子电容提供模拟技术的全面概述.
- 为突出模拟电极材料,电解质和SIC接口的进步.
- 确定挑战,并提出人工智能驱动的SIC设计的未来方向.
主要方法:
- 经典的分子动力学 经典的分子动力学
- 密度函数理论 (DFT) 是一种密度函数理论.
- 一开始的分子动力学 (AIMD)
- 量子力学/分子力学 (QM/MM) 的混合体.
- 机器学习 (ML) 强力场
主要成果:
- 模拟技术揭示了对电极材料设计 (纳米孔碳,MOF,MXenes) 的见解.
- 建模的进步优化了电解质特性 (溶解结构,离子运输) 和接口工程 (EDL结构,电容).
- 主要挑战包括尺度准确性权衡,样本采集不足和有限的可转移性.
结论:
- 未来的方向包括整合基于物理的ML,多尺度建模和LLM用于合理的SIC设计.
- 建议对离子储能进行数据驱动,人工智能辅助工程的范式转变.
- 这种方法旨在加速高性能SIC的开发.
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