A scalable kinetic Monte Carlo platform for charge transport dynamics in polymer-based memristive systems

Abstract

Polymer-assisted ion transport underpins both energy storage technologies and emerging neuromorphic computing devices. Efficient modeling of ion migration is essential for understanding the performance of batteries and memristors, but it remains challenging because of the interplay of drift, diffusion, and electrostatic interactions, as well as the limitations of continuum and molecular dynamics approaches. Addressing these challenges is particularly relevant in the context of the climate and energy crisis, where high-performance, low-carbon technologies require optimized ion-conducting materials and devices. Here, we introduce a scalable and lexible stochastic simulation platform that uses Markov chain Monte Carlo methodology to model ion migration in solid-state systems. The platform employs a vectorized, rail-based representation of device geometry, enabling rapid simulation of lateral ion transport and space-charge effects while preserving the stochastic nature of hopping events. It accommodates a wide range of material systems and can integrate experimental input parameters without code modification. We also provide an implementation of the model that takes advantage of highly energy-efficient GPUs, improving the performance and reducing the carbon footprint of the simulations. Validation using polymer-based memristive devices demonstrates the simulator’s ability to capture key behaviors, including relaxation decay, current–voltage hysteresis, spike-timing-dependent plasticity, and learning/forgetting rates. By balancing computational efficiency with mesoscale physical considerations, the platform provides a versatile tool for exploring ion-driven phenomena in energy storage and neuromorphic devices, supporting exploratory research.