About the ElementEmbeddings package
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The Element Embeddings package provides high-level tools for analysing elemental embeddings data. This primarily involves visualising the correlation between embedding schemes using different statistical measures.
- Documentation: https://wmd-group.github.io/ElementEmbeddings/
- Examples: https://github.com/WMD-group/ElementEmbeddings/tree/main/examples
Motivation
Machine learning approaches for materials informatics have become increasingly widespread. Some of these involve the use of deep learning techniques where the representation of the elements is learned rather than specified by the user of the model. While an important goal of machine learning training is to minimise the chosen error function to make more accurate predictions, it is also important for us material scientists to be able to interpret these models. As such, we aim to evaluate and compare different atomic embedding schemes in a consistent framework.
Developer
- Anthony Onwuli (Department of Materials, Imperial College London)
References
H. Park et al, "Mapping inorganic crystal chemical space" Faraday Discuss. (2024)