Accelerating Cathode Material Discovery through Ab Initio Random Structure Searching
Bonan Zhu, Ziheng Lu, Chris J. Pickard, David O. Scanlon

TL;DR
This paper demonstrates that ab initio random structure searching (AIRSS) can efficiently identify both known and novel low-energy cathode material structures solely based on chemical composition, accelerating discovery in Li-ion battery research.
Contribution
The study introduces AIRSS as a data-independent method for predicting complex cathode structures, including unknown polymorphs, enhancing materials discovery beyond existing databases.
Findings
Successfully reproduced known cathode polymorphs
Predicted new, more stable polymorphs of LiFeSO4F
Identified potential new redox-active phases for cathodes
Abstract
The choice of cathode material in Li-ion batteries (LIBs) underpins their overall performance. Discovering new cathode materials is a slow process, and all major commercial cathode materials are still based on those identified in the 1990s. Materials discovery using high-throughput calculations has attracted great research interest, however, reliance on databases of existing materials begs the question of whether these approaches are applicable for finding truly novel materials. In this work, we demonstrate that ab-initio random structure searching (AIRSS), a first-principles structure prediction methods that does not rely on any pre-existing data, can locate low energy structures of complex cathode materials efficiently based only on chemical composition. We use AIRSS to explore three Fe-containing polyanion compounds as low-cost cathodes. Using known quaternary LiFePO4 and quinary…
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