Element similarity in high-dimensional materials representations

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.Comment: 7 pages, 8 figure

Element similarity in high-dimensional materials representations

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

Mapping inorganic crystal chemical space

The combination of elements from the Periodic Table defines a vast chemical space. Only a small fraction of these combinations yield materials that occur naturally or are accessible synthetically. Here, we enumerate binary, ternary, and quaternary element combinations to produce an extensive library of over 10^10 stoichiometric inorganic compositions. The unique combinations are vectorised using compositional embeddings drawn from a variety of published machine-learning models. Dimensionality reduction techniques are employed to present a two-dimensional representation of inorganic crystal-chemical space, which is labelled according to whether they pass standard chemical filters and if they appear in known materials databases