Element similarity in high-dimensional materials representations

Anthony Onwuli, Ashish V. Hegde, Kevin V.T. Nguyen, Keith T. Butler, Aron Walsh

Research output: Contribution to journalArticlepeer-review


The traditional display of elements in the periodic table is convenient for the study of chemistry and physics. However, the atomic number alone is insufficient for training statistical machine learning models to describe and extract composition-structure-property relationships. Here, we assess the similarity and correlations contained within high-dimensional local and distributed representations of the chemical elements, as implemented in an open-source Python package ElementEmbeddings. These include element vectors of up to 200 dimensions derived from known physical properties, crystal structure analysis, natural language processing, and deep learning models. A range of distance measures are compared and a clustering of elements into familiar groups is found using dimensionality reduction techniques. The cosine similarity is used to assess the utility of these metrics for crystal structure prediction, showing that they can outperform the traditional radius ratio rules for the structural classification of AB binary solids.

Original languageEnglish
Pages (from-to)1558-1564
Number of pages7
JournalDigital Discovery
Issue number5
StatePublished - 12 Sep 2023

Bibliographical note

Publisher Copyright:
© 2023 RSC


Dive into the research topics of 'Element similarity in high-dimensional materials representations'. Together they form a unique fingerprint.

Cite this