Machine Learned Synthesizability Predictions Aided by Density Functional Theory

Accurately predicting a material's synthesizability remains a grand challenge in materials science. From early heuristics like Pauling's Rules to density functional theory (DFT) calculations, there are a wide variety of approaches to solving this challenge. Machine learning and data-driven approaches have recently made significant progress, yet some works do not account for phase stability. Here, we demonstrate that stability calculated from DFT plays a crucial role in enabling a machine learning model to accurately predict half-Heusler synthesizability. Our model takes ternary 1:1:1 compositions and predicts synthesizabilities in the half-Heusler structure, achieving a precision of 0.82 and recall of 0.82. Our model identifies 121 synthesizable candidates out of 4141 unreported compositions. 39 stable compositions are predicted unsynthesizable while 62 unstable compositions are predicted synthesizable; these findings otherwise cannot be made using DFT alone.