论文标题
基于物理的机器学习发现了用于纳米多孔电极中非线性离子传输的纳米电路
Physics-based Machine Learning Discovered Nano-circuitry for Nonlinear Ion Transport in Nanoporous Electrodes
论文作者
论文摘要
纳米多孔离子系统涉及密闭离子运输。但是，由于电路理论与基础物理化学之间的差距，使用电路模型来机械学预测其电气设计和性能评估的电气特性是一项挑战。在这里，我们证明了机器学习可以弥合这一差距并基于从修改后的Poisson-Nernst-Nernst-Nernst-Nernst-Nernst-Nernst-Nernst-Nernst-Nernst-Nernst-Nernst-Nernst-Nernst-Nernst-Nernst-nernst-Planck仿真结果中产生基于物理的纳米通路，在这种情况下，揭露了受到封闭离子的异常建设性的构建性扩散式迁移相互作用。这种桥接技术使我们能够获得纳米多孔电极中离子动力学的物理见解，例如非理想的环状伏安法。
Confined ion transport is involved in nanoporous ionic systems. However, it is challenging to mechanistically predict its electrical characteristics for rational system design and performance evaluation using electrical circuit model due to the gap between the circuit theory and the underlying physical chemistry. Here we demonstrate that machine learning can bridge this gap and produce physics-based nano-circuitry, based on equation discovery from the modified Poisson-Nernst-Planck simulation results where an anomalous constructive diffusion-migration interplay of confined ions is unveiled. This bridging technique allows us to gain physical insights of ion dynamics in nanoporous electrodes, such as the non-ideal cyclic voltammetry.
