Machine learned synthesizability predictions aided by density functional theory

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

doi:10.1038/s43246-022-00295-7

Title: Machine learned synthesizability predictions aided by density functional theory

Journal Article · 11 October 2022 · Communications Materials

DOI: https://doi.org/10.1038/s43246-022-00295-7 · OSTI ID:1891929

; Sarker, Suchismita; Saal, James E.;

; Borg, Christopher; Mehta, Apurva; Wolverton, Christopher

Abstract 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.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 1891929

Journal Information:: Communications Materials, Journal Name: Communications Materials Journal Issue: 1 Vol. 3; ISSN 2662-4443

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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