Universal ion-transport descriptors and classes of inorganic solid-state electrolytes
Cibr\'an L\'opez, Agust\'i Emperador, Edgardo Saucedo, Riccardo, Rurali, Claudio Cazorla

TL;DR
This study uses data-driven methods to identify universal descriptors for inorganic solid-state electrolytes, revealing that vibrational and elastic properties are key to classifying and understanding ion conductivity in these complex materials.
Contribution
It introduces a novel data analysis approach that highlights vibrational and elastic descriptors as fundamental for classifying solid-state electrolytes, advancing rational design principles.
Findings
Vibrational descriptors correlate strongly with ion diffusivity.
Elastic and vibrational properties are more effective for classification than chemical composition.
Database inclusion of temperature effects is crucial for better understanding.
Abstract
Solid-state electrolytes (SSE) 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, SSE are extremely complex materials for which general rational design principles remain indeterminate. Here, we unite 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 analysis show that elastic and…
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Taxonomy
TopicsMachine Learning in Materials Science · Fuel Cells and Related Materials · Solid-state spectroscopy and crystallography
