Fig. 1.

Atom2Vec workflow to learn atoms from the materials database. Atom–environment pairs are generated for every compound in the materials database, based on which atom–environment matrix is constructed. A small dataset of seven compounds is used here as an example. Entries of the atom–environment matrix denote the numbers of atom–environment pairs. Inset shows the unit cell of compound $B{i}_{2}S{e}_3$ and the pair corresponding to the entry of target atom $Bi$ and environment $\left(2\right)Se3$. Only compositional information is considered, while structural information is ignored. Atom2Vec learning algorithms extract knowledge of atoms from the atom–environment matrix and encode learned properties in atom vectors.