computational_eletrocatalysis

Open educational notebooks and computational workflows for learning computational electrocatalysis.

Computational Electrocatalysis

An Integrated Learning Pathway for Computational Electrocatalysis

This repository provides open interactive Python notebooks, computational models, datasets, and machine-learning tutorials for learning computational electrocatalysis.

The learning pathway integrates three interconnected components:

1. Data Foundation – computational electrocatalyst data, models, and open data practices.
2. Learning Pathway – interactive and structured learning activities.
3. ML – machine-learning tutorials, benchmarking, and model interpretation.

The educational materials are designed to support reproducible and hands-on learning using Python and Google Colab.

Learning pathway

Data Foundation → Tool for Higher Education → ML Pedagogy

The course progresses through data discovery, computational model construction, visualization, data analysis, machine learning, and interpretation.

Repository structure

• 01_Data_Foundation/ – Computational models and data resources
• 02_Learning_Pathway/ – Interactive course modules
• 03_ML/ – Machine-learning tutorials and workflows
• notebooks/ – Jupyter notebooks
• exercises/ – Student activities
• solutions/ – Reference solutions

How to use

The notebooks can be opened and executed directly using Google Colab. No local installation is required for most learning activities.

Status

This repository is under active development.