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Specialized hardware for neural networks requires materials with tunable symmetry, retention, and speed at low power consumption. The study proposes lithium titanates, originally developed as Li‐ion battery anode materials, as promising candidates for memristive‐based neuromorphic computing hardware. By using ex‐ and in operando spectroscopy to monitor the lithium filling and emptying of structural...
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