This app provides a detailed notebook introducing a convenient tool for developing Machine Learning Interatomic Potentials (ML-IAPs) for solid-state materials. Readers who are interested in using the tool can run the notebook on nanohub platform and go check http://github.com/materialsvirtuallab/maml for further details.

Researchers should cite this work as follows:

• Chi Chen, Yunxing Zuo (2021), "Machine Learning Force Field for Materials," https://nanohub.org/resources/maml. (DOI: 10.21981/71AF-S973).

  BibTex | EndNote

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