arXiv Open Access 2020

Physics-based Machine Learning Discovered Nano-circuitry for Nonlinear Ion Transport in Nanoporous Electrodes

Hualin Zhan Richard Sandberg Fan Feng Qinghua Liang Ke Xie +3 lainnya

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Abstrak

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.

Topik & Kata Kunci

cond-mat.mes-hall physics.app-ph physics.chem-ph physics.comp-ph physics.flu-dyn

Penulis (8)

Hualin Zhan

Richard Sandberg

Fan Feng

Qinghua Liang

Ke Xie

Lianhai Zu

Dan Li

Jefferson Zhe Liu

Format Sitasi

APA MLA BibTeX

Zhan, H., Sandberg, R., Feng, F., Liang, Q., Xie, K., Zu, L. et al. (2020). Physics-based Machine Learning Discovered Nano-circuitry for Nonlinear Ion Transport in Nanoporous Electrodes. https://arxiv.org/abs/2010.08151

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Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓