Machine Learning-Accelerated Discovery of A2BC2 Ternary Electrides with Diverse Anionic Electron Densities.

ABSTRACT

This study combines machine learning (ML) and high-throughput calculations to uncover new ternary electrides in the A2BC2 family of compounds with the P4/mbm space group. Starting from a library of 214 known A2BC2 phases, density functional theory calculations were used to compute the maximum value of the electron localization function, indicating that 42 are potential electrides. A model was then trained on this data set and used to predict the electride behavior of 14,437 hypothetical compounds generated by structural prototyping. Then, the stability and electride features of the 1254 electride candidates predicted by the model were carefully checked by high-throughput calculations. Through this tiered approach, 41 stable and 104 metastable new A2BC2 electrides were predicted. Interestingly, all three kinds of electrides, i.e., electron-deficient, electron-neutral, and electron-rich electrides, are present in the set of predicted compounds. Three of the most promising new electrides (two electron-rich, Nd2ScSi2 and La2YbGe2, and one electron-deficient Y2LiSi2) were then successfully synthesized and characterized experimentally. Furthermore, the synthesized electrides were found to exhibit high catalytic activities for NH3 synthesis under mild conditions when Ru-loaded. The electron-deficient Y2LiSi2, in particular, was seen to exhibit a good balance of catalytic activity and chemical stability, suggesting its future application in catalysis.