Predicting Voltammetry Using Physics-Informed Neural Networks

[Image: see text] We propose a discretization-free approach to simulation of cyclic voltammetry using Physics-Informed Neural Networks (PINNs) by constraining a feed-forward neutral network with the diffusion equation and electrochemically consistent boundary conditions. Using PINNs, we first predic...

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Detalles Bibliográficos
Autores principales: Chen, Haotian, Kätelhön, Enno, Compton, Richard G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9084599/
https://www.ncbi.nlm.nih.gov/pubmed/35007069
http://dx.doi.org/10.1021/acs.jpclett.1c04054

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9084599/
https://www.ncbi.nlm.nih.gov/pubmed/35007069
http://dx.doi.org/10.1021/acs.jpclett.1c04054

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