Machine Learned Synthesizability Predictions Aided by Density Functional Theory

Abstract A grand challenge of materials science is the computational prediction of synthesis pathways for novel compounds. Data-driven and machine learning (ML) approaches have made significant progress in addressing a portion of this problem, namely, predicting whether a compound is synthesizable or not. However, some recent attempts to do so have not incorporated energetic or phase stability information. Here, we combine thermodynamic stability calculated using density functional theory (DFT) with composition-based features to train a ML model that predicts a material's synthesizability. We focus on compositions with ABC stoichiometry and predict synthesizability in the half-heusler (HH) structure. Our model is trained on experimentally reported ABC compositions, and achieves a cross-validated precision of 0.83 and recall of 0.85. Our model shows improvement in identifying compositions that do not form HH when compared to a similar HH synthesizability model from a previous study. Our model identifies 163 synthesizable candidates out of 4141 unreported ABC compositions. More notably, 38 stable compositions are predicted unsynthesizable while 103 unstable compositions are predicted synthesizable; these findings otherwise cannot be made using DFT stability alone. This study presents a new approach for accurately predicting synthesizability, and identifies newly synthesizable candidate HH compositions for further experimental exploration.