A General Molecular-Scale Dynamic Memristor Model Based on Non-equilibrium Charge Transport Kinetics and Its Information Processing Capability in Reservoir Computing

TL;DR

This paper presents a comprehensive molecular-scale memristor model based on non-equilibrium charge transport kinetics, demonstrating its potential for neuromorphic computing and reservoir computing applications.

Contribution

It introduces a novel dynamic memristor model integrating electron transport theories with slow process kinetics, linking molecular dynamics to neuromorphic computing capabilities.

Findings

The model reproduces experimental conductance hysteresis.

It emulates synaptic functions like STP and STDP.

Performance in reservoir computing depends on input frequency and bias range.

Abstract

Non-equilibrium 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 like short-term plasticity (STP) and spike-timing-dependent plasticity (STDP). By incorporating the model into a reservoir computing (RC) 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-equilibrium molecular-scale dynamics can drive information processing in the post-Moore era.