Machine learned synthesizability predictions aided by density functional theory

Andrew Lee, Suchismita Sarker, James E. Saal, Logan Ward, Christopher Borg, Apurva Mehta, Christopher Wolverton*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


A grand challenge of materials science is predicting synthesis pathways for novel compounds. Data-driven approaches have made significant progress in predicting a compound’s synthesizability; however, some recent attempts ignore phase stability information. Here, we combine thermodynamic stability calculated using density functional theory with composition-based features to train a machine learning model that predicts a material’s synthesizability. Our model predicts the synthesizability of ternary 1:1:1 compositions in the half-Heusler structure, achieving a cross-validated precision of 0.82 and recall of 0.82. Our model shows improvement in predicting non-half-Heuslers compared to a previous study’s model, and identifies 121 synthesizable candidates out of 4141 unreported ternary compositions. More notably, 39 stable compositions are predicted unsynthesizable while 62 unstable compositions are predicted synthesizable; these findings otherwise cannot be made using density functional theory stability alone. This study presents a new approach for accurately predicting synthesizability, and identifies new half-Heuslers for experimental synthesis.

Original languageEnglish (US)
Article number73
JournalCommunications Materials
Issue number1
StatePublished - Dec 2022

ASJC Scopus subject areas

  • General Materials Science
  • Mechanics of Materials


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