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Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials.

Matthew J McDermottBrennan C McBrideCorlyn E RegierGia Thinh TranYu ChenAdam A CorraoMax C GallantGabrielle E KammChristopher J BartelKarena W ChapmanPeter G KhalifahGerbrand CederJames R NeilsonKristin Aslaug Persson
Published in: ACS central science (2023)
Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and secondary competition, to assess the favorability of target/impurity phase formation in solid-state reactions. We used these metrics to analyze 3520 solid-state reactions in the literature, ranking existing approaches to popular target materials. Additionally, we implemented these metrics in a data-driven synthesis planning workflow and demonstrated its application in the synthesis of barium titanate (BaTiO 3 ). Using an 18-element chemical reaction network with first-principles thermodynamic data from the Materials Project, we identified 82985 possible BaTiO 3 synthesis reactions and selected 9 for experimental testing. Characterization of reaction pathways via synchrotron powder X-ray diffraction reveals that our selectivity metrics correlate with observed target/impurity formation. We discovered two efficient reactions using unconventional precursors (BaS/BaCl 2 and Na 2 TiO 3 ) that produce BaTiO 3 faster and with fewer impurities than conventional methods, highlighting the importance of considering complex chemistries with additional elements during precursor selection. Our framework provides a foundation for predictive inorganic synthesis, facilitating the optimization of existing recipes and the discovery of new materials, including those not easily attainable with conventional precursors.
Keyphrases
  • solid state
  • small molecule
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  • high throughput
  • magnetic resonance imaging
  • magnetic resonance
  • quality improvement
  • water soluble
  • artificial intelligence