A Generalizable Machine Learning Framework for Identifying Sustainable Multi-Ion Garnet Electrolytes.

Lithium-ion (Li-ion) solid-state batteries (SSBs) are highly regarded for their exceptional energy density and prolonged operational lifespan. However, concerns regarding their sustainability have arisen due to the uneven global distribution of Li resources and Li's relatively low abundance in the Earth's crust. Consequently, significant interest has shifted toward developing alternative SSBs, such as sodium (Na), magnesium (Mg) and Aluminum (Al)-ion batteries. A key challenge in this pursuit is efficiently identifying viable solid-state electrolytes (SEs) from the vast chemical space, particularly for Na and Mg ions. This study introduces a generalized framework based on machine learning for effectively screening high-performance garnet-type SEs. Utilizing specifically designed chemical descriptors, ML models predict the thermal stability and electrical conductivity of garnet-type SEs, achieving predictive accuracies of 94% and 89%, respectively. The chemical factors influencing stability and conductivity are identified and validated through interpretability analysis. Leveraging these models, 1764 garnet-type SEs exhibiting high thermal stability and wide band gaps were screened from a database of 43,732 compounds. Furthermore, 44 garnet-type SEs with favorable environmental and economic advantages were selected, and verified through first-principles calculations using density functional theory. Given their cost-effectiveness and high performance, these SEs hold great potential for application in Na, Mg, and Al ion SSBs. This study provides crucial insights into developing SSB materials, advances sustainable energy storage, and offers key perspectives for exploring material systems within specific space groups.