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

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.