Discovery of metastable P–Si–Sn ternary compounds aided by machine learning

The crystalline structures of energetically favorable P–Si–Sn ternary compounds containing two group 14 elements Si and Sn are explored using a combined machine learning approach and first-principles calculations. While no binary Si-Sn compounds have been reported in the literature, several energetically favorable metastable P–Si–Sn ternary compounds have been predicted from this study. The crystalline structures and electronic properties of five low-energy metastable compounds, namely, Pnma-P3SiSn, Cmc21-P5SiSn2, Pnma-P2SiSn, P21/c-P14Si8Sn3, and P212121-P4SiSn, are investigated. In these compounds, Si and Sn atoms are separated by P atoms, forming distinct structural motifs, such as Si-centered tetrahedra (SiP4), Sn-centered octahedra (SnP6), and P chains. The electronic properties vary among these metastable compounds: Pnma-P3SiSn and Cmc21-P5SiSn2 exhibit metallic behavior, whereas Pnma-P2SiSn, P21/c-P14Si8Sn3, and P212121-P4SiSn are semiconductors. These compounds were dynamically stable at ambient conditions, indicating the possibility of experimental synthesis. The results demonstrate the effectiveness of combining machine learning and first-principles approaches to discover and characterize novel materials, thereby providing valuable insights into the advancement of materials science and technology.