A general molecular-scale dynamic memristor model based on non-steady-state charge transport kinetics and its information processing capability in reservoir computing

Non-steady-state molecular-scale dynamics, where fast electron transport couples with slow chemical state evolution, underpins the complex behaviors of molecular memristors, yet a general model linking these dynamics to neuromorphic computing remains elusive. We introduce a dynamic memristor model that integrates Landauer and Marcus electron transport theories with the kinetics of slow processes, such as proton/ion migration or conformational changes. This framework reproduces experimental conductance hysteresis and emulates synaptic functions such as short-term plasticity and spike-timing-dependent plasticity. By incorporating the model into a reservoir computing architecture, we show that computational performance optimizes when input frequency and bias mapping range align with the molecular system’s intrinsic kinetics. This chemistry-centric, bottom-up approach provides a theoretical foundation for molecular-scale neuromorphic computing, demonstrating how non-steady-state molecular-scale dynamics can drive information processing in the post-Moore era.