This tutorial introduces the concepts of machine learning interatomic potentials (ML-IAPs) in materials science, including two components of local environment atomic descriptors and machine learning models. Using the prepared dataset, you will learn how to build a prototype ML-IAP and use it to predict basic material properties for a multi-component system.

Network for Computational Nanotechnology (NCN)

Researchers should cite this work as follows:

• Yunxing Zuo (2021), "Convenient and efficient development of Machine Learning Interatomic Potentials," https://nanohub.org/resources/34745.

  BibTex | EndNote

1. density functional theory (DFT)
2. first principles
3. hands on
4. interatomic potentials
5. machine learning
6. material properties
7. materials discovery
8. materials science
9. NCN Group - Machine Learning
10. NCN Group - Machine Learning Hands-on
11. tutorial