Enabling new electrochemical technologies requires systems that can operate under ever-more demanding conditions, and progress in energy storage applications reveals tantalizing opportunities to reimagine electrolyte design for performance at extreme potentials. A common thread among these innovations is the formation of significant populations of contact ion pairs (CIPs) in the electrolyte, regardless of the specific cation chemistry or solvent system. The examples summarized in this review suggest that a set of general electrolyte design rules likely exists, where the purposeful selection of anion chemistry can yield CIP structures with tunable control over reaction thermodynamics, kinetics, and interphase chemistry. Identifying the relevant descriptors for high-performance, anion-derived CIP structures can be achieved utilizing a combined experimental and computational approach, aided by machine learning and artificial intelligence, to more rapidly survey the vast combinatorial space available and to enable a new generation of electrolytes for decarbonized electrochemical processes at scale.

中文翻译：

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阴离子衍生的接触离子对作为电解质设计的统一原则


实现新的电化学技术需要能够在越来越苛刻的条件下运行的系统，而储能应用的进步为重新构想电解质设计以在极端电位下的性能带来了诱人的机会。这些创新之间的一个共同点是在电解质中形成大量接触离子对 （CIP），而与特定的阳离子化学或溶剂系统无关。本综述中总结的示例表明，可能存在一套通用的电解质设计规则，其中有目的地选择阴离子化学可以产生 CIP 结构，对反应热力学、动力学和界面化学具有可调控制。在机器学习和人工智能的辅助下，可以利用结合实验和计算方法来确定高性能阴离子衍生 CIP 结构的相关描述符，以更快地研究可用的巨大组合空间，并为大规模脱碳电化学过程实现新一代电解质。