Post Li-ion (Na, K, Ca, Zn and so on) battery materials design and discovery.

Meeting the escalating demand for electric energy necessitates advancements in battery technology with higher energy density. Solid-state batteries, heralded as a climate-neutral solution, exhibit promisingly high energy densities. However, persistent challenges lie in discovering solid-state electrolytes characterized by both high ionic conductivity and stable electrode interfaces. Beyond this, alternative ion batteries, including Sodium-ion (Na), Potassium-ion (K), Magnesium-ion (Mg), or dual-ion configurations, play a pivotal role in sustainable development. Unearthing novel electrode or electrolyte materials for these alternative ion batteries is essential. Sodium-ion batteries (SIBs) have emerged as a compelling alternative to lithium-ion batteries (LIBs), particularly for large-scale energy storage. Leveraging sodium's abundance, eco-friendliness, and cost-effectiveness, SIBs hold promise. Noteworthy progress has been achieved in developing room-temperature SIBs employing organic liquid electrolytes. However, safety concerns persist, mirroring challenges faced by their lithium counterparts due to the volatile and flammable nature of liquid electrolytes.

A common challenge faced by "beyond Li-ion" batteries involves the necessity to develop or discover new materials tailored to current battery technology. Addressing this challenge, our goal is to predict novel materials for post Li-ion batteries using structure prediction methods such as USPEX (universal structure predictor: evolutionary crystallography), CE (cluster expansion), and DMSP (data-mined structure predictor). However, predicting the crystal structure of solid-state electrolytes or cathode materials, comprised of diverse atom types, poses computational challenges. To overcome this hurdle, we are exploring the integration of machine learning and orbital-free DFT (OF-DFT) methods in conjunction with USPEX or CE.