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A critical evaluation of using physics-informed neural networks for simulating voltammetry: strengths, weaknesses and best practices

Abstract:

The recent explosion of applications of physics-informed neural networks (PINNs) as a discretization-free tool to solve partial differential equations (PDEs) shows great potential for applications in electroanalytical simulations. However, a simple, naive PINN approach may fail to make analytical level predictions in even only moderately complicated systems. Here, we explore eight test cases, spanning 1D to 3D simulations, including both cyclic voltammetry and chronoamperometry, and a wide...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.jelechem.2022.116918

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Chemistry
Oxford college:
Lady Margaret Hall
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Chemistry
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Chemistry
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Chemistry
Sub department:
Physical & Theoretical Chem
Oxford college:
St John's College
Role:
Author
Publisher:
Elsevier
Journal:
Journal of Electroanalytical Chemistry More from this journal
Volume:
925
Publication date:
2022-10-20
Acceptance date:
2022-10-16
DOI:
ISSN:
1572-6657
Language:
English
Keywords:
Pubs id:
1299993
Local pid:
pubs:1299993
Deposit date:
2022-11-11

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