Journal article

Machine learning discovered nano-circuitry for nonlinear ion transport in nanoporous materials

Hualin Zhan, Richard Sandberg, Zhiyuan Xiong, Qinghua Liang, Ke Xie, Lianhai Zu, Dan Li, Jefferson Zhe Liu

Research Square

Abstract

Abstract Connecting physio-chemical theory with electrical model is essential yet difficult for evaluating the impact of nonlinear ion transport on the performance of ionic circuits and electrochemical energy storage devices1-6. Here we demonstrate that machine learning can resolve this difficulty and produce physics-based nano-circuitry. Starting from a physio-chemical perspective, we first reveal an anomalous diffusion-enhanced migration of ions in nanopores, which exhibits a nonlinear electrical response. Using machine learning, we discover its underlying mathematical equation, and produce a dynamically varying ionic resistance for construction of nano-circuitry model. Based on th..

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