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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2010.08151 |
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| _version_ | 1866916266984341504 |
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| author | Zhan, Hualin Sandberg, Richard Feng, Fan Liang, Qinghua Xie, Ke Zu, Lianhai Li, Dan Liu, Jefferson Zhe |
| author_facet | Zhan, Hualin Sandberg, Richard Feng, Fan Liang, Qinghua Xie, Ke Zu, Lianhai Li, Dan Liu, Jefferson Zhe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2010_08151 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Physics-based Machine Learning Discovered Nano-circuitry for Nonlinear Ion Transport in Nanoporous Electrodes Zhan, Hualin Sandberg, Richard Feng, Fan Liang, Qinghua Xie, Ke Zu, Lianhai Li, Dan Liu, Jefferson Zhe Mesoscale and Nanoscale Physics Applied Physics Chemical Physics Computational Physics Fluid Dynamics 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. |
| title | Physics-based Machine Learning Discovered Nano-circuitry for Nonlinear Ion Transport in Nanoporous Electrodes |
| topic | Mesoscale and Nanoscale Physics Applied Physics Chemical Physics Computational Physics Fluid Dynamics |
| url | https://arxiv.org/abs/2010.08151 |