Discovery of Ternary Antimonides A–Al–Sb (A = Rb or Cs) with Desired Structural Motifs Guided by Machine Learning

Abstract

Specific structural motifs in inorganic solids are often related to their targeted physical properties. For many classes of solids, such as Zintl phases and polar intermetallics, the crystal structures are diverse and not easy to predict. Various antimonides that are potential thermoelectric materials were proposed to be synthesizable on the basis of their estimated formation energies. Their structures were broadly classified as clathrate, channel, layered, or network through a machine learning model trained on existing ternary phases and features based on elemental properties using the sure independence screening and sparsifying operator algorithm. Through experimental validation, three new ternary antimonides were synthesized and confirmed to form layered structures: tetragonal RbAlSb₂ and CsAlSb₂, which are isopointal but not isotypic to LiBSi₂; and monoclinic Rb₂Al₂Sb₃, which adopts the Na₂Al₂Sb₃-type structure. Reinvestigation of the related compound Cs₂In₂Sb₃ revealed a low thermal conductivity and p-type semiconducting behavior.