综合监督和无监督机器学习方法用于映射电化学窗口 超过4500种溶剂用于电池应用
Souvik Manna1, Surya Sekhar Manna1, Biswarup Pathak1
1Department of Chemistry, Indian Institute of Technology (IIT) Indore, Simrol, Indore 453552, India.
ACS applied materials & interfaces
|July 31, 2024
概括
机器学习加速了为高压可充电电池发现最佳溶剂电解质的发现. 这种方法可以准确地预测电解质与所需的电化学窗口 (ECW),减少实验试验和错误.
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:
- 可充电金属离子电池的开发受到溶剂电解质和高压电极材料不兼容的阻碍.
- 从广的化学空间中识别具有优化电化学窗口 (ECW) 的合适溶剂电解质是一个重大挑战.
研究的目的:
- 开发一种机器学习 (ML) 方法,以快速识别具有所需ECW的溶剂电解质.
- 为了减少电池应用的潜在溶剂电解质的搜索空间.
主要方法:
- 采用了联合监督和无监督 (K-means集群) 机器学习策略.
- 监督的ML准确地预测了4882个候选溶剂中的最佳溶剂电解质.
- 无监督集群识别了基于ECW范围的11个不同的溶剂集群.
主要成果:
- 该ML模型在预测目标ECW的溶剂电解质方面表现出高准确性.
- 聚类可以有效地加速减少溶剂搜索空间.
- 这种方法在准确性方面明显优于以前的密度函数理论 (DFT) 方法.
结论:
- 拟议的ML方法有效地简化了用于先进电池应用的合适溶剂电解质的识别.
- 这种计算方法最大限度地减少了资源支出,避免了昂贵的试错实验.
- 该战略加速了下一代可充电电池的发展.
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