Universal ion-transport descriptors and classes of inorganic solid-state electrolytes

Solid-state electrolytes (SSEs) with high ion conductivity are pivotal for the development and large-scale adoption of green-energy conversion and storage technologies such as fuel cells, electrocatalysts and solid-state batteries. Yet, SSEs are extremely complex materials for which general rational design principles remain indeterminate. Here, we combine first-principles materials modelling, computational power and modern data analysis techniques to advance towards the solution of such a fundamental and technologically pressing problem. Our data-driven survey reveals that the correlations between ion diffusivity and other materials descriptors in general are monotonic, although not necessarily linear, and largest when the latter are of vibrational nature and explicitly incorporate anharmonic effects. Surprisingly, principal component and k-means clustering analyses show that elastic and vibrational descriptors, rather than the usual ones related to chemical composition and ion mobility, are best suited for reducing the high complexity of SSEs and classifying them into universal classes. Our findings highlight the need for considering databases that incorporate temperature effects to improve our understanding of SSEs and point towards a generalized approach to the design of energy materials.
We acknowledge financial support from the MCIN/AEI/10.13039/501100011033 under Grant no. PID2020-119777GB-I00, the “Ramón y Cajal” fellowship RYC2018-024947-I, the Severo Ochoa Centres of Excellence Program (CEX2019-000917-S), the Generalitat de Catalunya under Grant no. 2017SGR1506, and the CSIC under the “JAE Intro SOMdM 2021” grant program. The authors acknowledge computational support from the Red Española de Supercomputación (RES) under the grants FI-2022-1-0006, FI-2022-2-0003 and FI-2022-3-0014.
With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000917-S).