Computationally Guided Synthesis of Battery Materials

Abstract

Materials synthesis is a critical step in the development of energy storage technologies, from the first synthesis of newly predicted materials to the optimization of key properties for established materials. While the synthesis of solid-state materials has traditionally relied on intuition-driven trial-and-error, computational approaches are now emerging to accelerate the identification of improved synthesis recipes. In this Perspective, we explore these techniques and focus on their ability to guide precursor selection for solid-state synthesis. The applicability of each method is discussed in the context of materials for batteries, including Li-ion cathodes and solid electrolytes for all-solid-state batteries. Our analysis showcases the effectiveness of these computational methods while also highlighting their limitations. Based on these findings, we provide an outlook on future developments that can address existing limitations and make progress toward synthesis-by-design for battery materials.