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

Abstract

Polymer assisted ion transport plays a central role in both energy storage technologies and emerging neuromorphic computing devices. Accurately modeling how ions move is crucial for understanding the behavior of batteries and memristors, yet it remains difficult due to the combined effects of drift, diffusion, and electrostatic interactions, along with the limits of continuum models and molecular dynamics. These issues are especially important in the context of the climate and energy crisis, where high performance, low carbon technologies rely on well optimized ion conducting materials and devices. In this work, we present a scalable and flexible stochastic simulation platform based on Markov chain Monte Carlo methods to model ion migration in solid state systems. The platform uses a vectorized, rail based description of device geometry, which allows fast simulations of lateral ion transport and space charge effects while keeping the inherently random nature of ion hopping. It supports a wide range of material systems and can incorporate experimental parameters without requiring changes to the code. We also introduce an implementation optimized for highly energy efficient GPUs, which boosts performance while lowering the carbon footprint of the simulations. Validation with polymer based memristive devices shows that the simulator captures key behaviors such as relaxation decay, current voltage hysteresis, and learning and forgetting dynamics. By combining computational efficiency with relevant mesoscale physics, this platform offers a practical and versatile tool for exploring ion driven processes in energy storage and neuromorphic devices, supporting exploratory and applied research.