Molecularly Engineered Memristors for Reconfigurable Neuromorphic Functionalities

For half a century, nanoelectronics has sought to tailor electrical properties through molecular design for electrical circuit components such as switches and memories, yet predictive models remain elusive. The challenge arises from the intrinsic complexity of structure–function relationships, where minute atomic-level changes in the molecule can trigger nonlinear, multi-pathway interactions in the charge transport layer. These interactions profoundly alter the device properties, obscuring the causal links between molecular composition and electrical response. Here, a predictive framework is established that integrates chemical synthesis and electrical transport measurements with ab-initio and quantum chemical modeling, to optimize the functionality and performance of neuromorphic circuit elements. Through precise tailoring of molecular coordination environments and outer-sphere ionic interactions in metal-organic ruthenium complexes, the device switching behavior is dramatically and programmatically modulated by accessing a rich spectrum of memristive responses, including digital, analog, binary, and ternary memory, spanning six orders of magnitude in conductance. It also enables the creation of a single circuit element that can dynamically reconfigure across diverse computational modalities, including in-memory logic, selector functions, analog storage, computation and synaptic plasticity. This work reimagines the traditional rubric of computing, creating materials that not only store and compute, but also adapt and reconfigure.