This is a code repository in company with "Predicting Voltammetry using Physics-Informed Neural Networks" published at The Journal of Physical Chemistry Letters
Python 3.7 and above is suggested to run the program. The neural networks was developed and tested with Tensorflow 2.3. To install required packages, run
$ pip install -r requirement.txt
- 1D Semi-Infinite: 1D simulation of cyclic voltammetry with semi-infinite boundary condition. A google colab notebook is also available.
- 1D Thin-Layer: 1D simulation at thin layer cyclic voltammetry
- 2D Microband: 2D simulation cyclic voltammetry at microband electrode
- 2D Square Electrode: 2D simulation of cyclic voltammetry at a square electrode
Please refer to SimulationDynamics.gif
A ready to run Google Colab Notebook is provided.
Please report any issues/bugs of the code in the discussion forum of the repository or contact the corresponding author of the paper
To cite, please refer to: J. Phys. Chem. Lett. 2022, 13, 2, 536–54
Since 2022, the authors have explored PINN's application in other fields of electrochemistry:
- Hydrodynamic voltammetry and channel electrode: Analyst, 2022,147, 1881-1891
- 3D cube electrode and best practices of PINN4Electrochemistry: J. Electroanal. Chem. 2022, 925, 116918
- Rotating disk electrode and edge effect: Anal. Chem. 2023, 95, 34, 12826–12834
- Rotating ring disk electrode:J. Phys. Chem. Lett. 2024, 15, 24, 6315–6324