Combinatorial methods in advanced battery materials design

In the search for better performing battery materials, researchers have increasingly ventured into complex composition spaces, including numerous pseudo-quaternaries, with further substitutions being either explored experimentally or proposed based on computation. Given vast spaces that need exploring, experimental combinatorial science can play an important role in accelerating development of advanced materials and is arguably best means to obtain a sufficiently large data set truly bring high degree precision computational techniques such as machine-learning. Herein, we present robust high-throughput synthesis platform currently used McCalla lab at McGill University study Li-ion cathodes, anodes, solid electrolytes, well Na-ion cathodes. The methods are presented detail, characterization utilize regularly (X-ray diffraction, electrochemical testing, impedance spectroscopy). We quantitatively determine reproducibility achieved by this system also demonstrate its versatility presenting first time two high-power anodes batteries (TiNb 2 O 7 W 3 Nb 14 44 ), state electrolyte Li La Zr 12 . Our reproduce accurately results from literature bulk samples, indicating methodology utilizing small milligram-scale samples scales up extremely larger sample sizes typically both industry. throughput infrastructure has current limit 896 XRD patterns EIS week 448 cyclic voltammograms running simultaneously.