Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC.

A method to predict the crystal structure of equiatomic ternary compositions based only on the constituent elements was developed using cluster resolution feature selection (CR-FS) and support vector machine (SVM) classification. The supervised machine-learning model was first trained with 1037 individual compounds that adopt the most populated ternary 1:1:1 structure types (TiNiSi-, ZrNiAl-, PbFCl-, LiGaGe-, YPtAs-, UGeTe-, and LaPtSi-type) and then validated using an additional 519 compounds. The CR-FS algorithm improves class discrimination and indicates that 113 variables including size, electronegativity, number of valence electrons, and position on the periodic table (group number) influence the structure preference.

Manganese-substituted rare-earth zinc arsenides RE(1-y)Mn(x)Zn(2-x)As2 (RE = Eu-Lu) and RE(2-y)Mn(x)Zn(4-x)As4 (RE = La-Nd, Sm, Gd).

Two series of Mn-substituted rare-earth zinc arsenides RE(1-y)Mn(x)Zn(2-x)As2 (RE = Eu-Lu) and RE(2-y)Mn(x)Zn(4-x)As4 (RE = La-Nd, Sm, Gd) were prepared by reaction of the elements at 750 °C. Both series are derived from ideal empirical formula REM2As2 (M = Mn, Zn) and adopt crystal structures related to the trigonal CaAl2Si2-type (space group P3m1) in which hexagonal nets of RE atoms and [M2As2] slabs built up of edge-sharing M-centered tetrahedra are alternately stacked along the c-direction. For compounds with divalent RE components (Eu, Yb), the fully stoichiometric and charge-balanced formula REM2As2 is obtained, with Mn and Zn atoms statistically disordered within the same tetrahedral site.