This tutorial covers materials graph networks for modeling crystal and molecular properties. We will introduce the graph representation of crystals and molecules and how the convolutional operations are carried out on the materials graphs. Then we will introduce the megnet package for constructing the graph network models, setting up the model for training and the model evaluation. We will show examples of graph models using computational data from the Materials Project database.

The nanoHUB tool "megnet: Materials Graph Network" used in this hands-on tutorial.

Network for Computational Nanotechnology (NCN)

Researchers should cite this work as follows:

• Chi Chen (2021), "Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks," https://nanohub.org/resources/34746.

  BibTex | EndNote

1. crystal structures
2. graph networks
3. hands on
4. machine learning
5. material discovery
6. material properties
7. materials science
8. NCN Group - Machine Learning
9. NCN Group - Machine Learning Hands-on
10. tutorial