一种可扩展的动力学蒙特卡洛平台，可对聚合物基忆阻系统中的电荷输运动力学进行全面模拟
A scalable kinetic Monte Carlo platform enabling comprehensive simulations of charge transport dynamics in polymer-based memristive systems
AI总结：
针对聚合物基忆阻系统中离子输运模拟的挑战，本文提出一种可扩展的动力学蒙特卡洛平台，利用GPU加速，能捕捉弛豫衰减等关键行为，为储能与神经形态器件研究提供工具。
摘要：
聚合物辅助离子传输是储能技术和新兴神经形态计算器件的基础。离子迁移的高效建模对于理解电池和忆阻器的性能至关重要，但由于漂移、扩散和静电相互作用的相互影响，以及连续介质和分子动力学方法的局限性，这仍然具有挑战性。在气候和能源危机的背景下，解决这些挑战尤为重要，因为高性能、低碳技术需要优化的离子导电材料和器件。在这里，我们介绍了一种可扩展且灵活的随机模拟平台，该平台使用马尔可夫链蒙特卡洛方法对固态系统中的离子迁移进行建模。该平台采用器件几何结构的矢量化、基于轨道的表示，能够快速模拟横向离子传输和空间电荷效应，同时保留跳跃事件的随机性。它适用于广泛的材料系统，无需修改代码即可集成实验输入参数。我们还提供了该模型的实现，利用了高能效GPU，提高了模拟性能并减少了碳足迹。使用聚合物基忆阻器件进行的验证表明，该模拟器能够捕捉关键行为，包括弛豫衰减、电流-电压滞后、脉冲时序依赖可塑性以及学习/遗忘率。通过在计算效率和介观物理考虑之间取得平衡，该平台为探索储能和神经形态器件中的离子驱动现象提供了一种多功能工具，支持探索性研究。
英文摘要：
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 flexible 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.
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