Development of novel approaches for designing advanced energy storage devices generally requires a fundamental understanding of the atomic structure of and processes in constituent materials. Correspondingly, the modern-day frameworks for computational design of electrolyte materials extensively employ either quantum calculations or atomistic simulation methods, or often, a combination thereof. Within the frameworks of recently devised approaches, these two computational methods are augmented by advanced machine learning techniques. Here, we focus on the recent developments in electrolyte materials design, with the emphasis on the computational design of liquid electrolytes. A particular attention is paid to the recent progress in building a unified framework for large-scale and high-throughput screening of liquid electrolyte material systems for battery applications. We address the status quo in the area and present a perspective on essential further efforts that have to be undertaken to construct realistic paradigms for intelligent design of electrolyte materials by testing physical and chemical properties of large sets of candidate chemical compounds and their combinations. The issues of further improvements in quantum calculations and atomistic simulation are also briefly addressed in the context of energy storage device applications.

开发用于设计高级能量存储设备的新颖方法通常需要对构成材料的原子结构和过程进行基本的了解。相应地，用于电解质材料的计算设计的现代框架广泛地采用了量子计算或原子模拟方法，或者经常采用其组合。在最近设计的方法的框架内，这两种计算方法通过高级机器学习技术得到了增强。在这里，我们重点介绍电解质材料设计的最新发展，重点是液体的计算设计电解质。特别需要注意的是最近在建立统一的框架以用于电池应用的液体电解质材料系统的大规模和高通量筛选方面的进展。我们着眼于该领域的现状，并就通过测试大量候选化学化合物及其组合的物理和化学性质来构建用于电解质材料智能设计的现实范式而必须进行的必要的进一步努力，提出了一种观点。在能量存储设备应用的背景下，还将简要介绍量子计算和原子模拟中进一步改进的问题。