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生成型人工智能导航了为下一代高性能电池开发溶剂的开发

Xiang Gao¹, Ao-Qi Yang¹, Wen-Bei Yu¹

¹College of Materials Science and Engineering, Fuzhou University, Fuzhou, Fujian, 350108, China.

Advanced materials (Deerfield Beach, Fla.)

|September 17, 2025

概括

研究人员开发了一种人工智能驱动的方法来选 (Mg) 电池的溶剂,识别了像DOX和DMP这样的有希望的候选人. 这种方法显著加快了发现高效电池材料的速度.

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:

传统的 (Mg) 金属电池的试错溶剂发现是低效和昂贵的.
下一代Mg电池需要新的电解质来提高性能和稳定性.

研究的目的:

通过整合人工智能 (AI) 和虚拟分子数据库,建立新型Mg电池溶剂的高效选标准.
为了彻底改变Mg电池电解质的传统溶剂设计途径.

主要方法:

使用自己开发的算法产生了823种潜在溶剂.
使用机器学习 (ML) 模型分析物理化学性质 (LUMO, ΔLUMO, ESPmin, ESPmax, Eb) 进行选.
实验验证了两种新型溶剂DOX和DMP,用于Mg电池应用.

主要成果:

从最初的823个池中确定了18种候选溶剂.
与Mg[B(hfip) [4]2盐相结合的DOX溶剂在Mg电池中显著提高了电化学性能.
在5200个周期的1.0mAcm-2.0的Mg涂层/剥离过程中获得了99.54%的平均库伦比效率.
/电池的累计容量超过2000 mAhcm-2,超过了以前的基准.

结论:

开发了一种基于物理化学性质的Mg电池溶剂简化选方法,将候选池减少了约45倍.
由人工智能驱动的方法显著提高了效率,并且可以扩展到其他电池系统.
验证了新型溶剂DOX和DMP,证明了它们对高性能Mg电池的潜力.

关键词:

多克斯多克斯多克斯多克斯生成型的人工智能 (GAI)mg金属电池的使用情况在溶剂选中使用溶剂选.虚拟分子数据库是一个虚拟的分子数据库.

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