SISSO files

Specific structural motifs in inorganic solids are often related to target 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 based on 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.

无机固体中的特定结构基元（structural motifs）通常与目标物理性质相关联。针对津特尔相（Zintl phases）、极性金属间化合物等诸多固体类别，其晶体结构多样且难以预测。研究人员基于估算得到的形成能，提出多种具备热电材料应用潜力的锑化物具备合成可行性。通过采用sure independence screening and sparsifying operator（SISSO）算法、基于现有三元相及元素性质特征训练得到的机器学习模型，研究人员将这些锑化物的结构大致划分为笼型、通道型、层状与网状四类。