A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis

Matthew J. McDermott, Shyam S. Dwaraknath and Kristin A. Persson ()
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Matthew J. McDermott: Lawrence Berkeley National Laboratory
Shyam S. Dwaraknath: Lawrence Berkeley National Laboratory
Kristin A. Persson: University of California

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract Accelerated inorganic synthesis remains a significant challenge in the search for novel, functional materials. Many of the principles which enable “synthesis by design” in synthetic organic chemistry do not exist in solid-state chemistry, despite the availability of extensive computed/experimental thermochemistry data. In this work, we present a chemical reaction network model for solid-state synthesis constructed from available thermochemistry data and devise a computationally tractable approach for suggesting likely reaction pathways via the application of pathfinding algorithms and linear combination of lowest-cost paths in the network. We demonstrate initial success of the network in predicting complex reaction pathways comparable to those reported in the literature for YMnO3, Y2Mn2O7, Fe2SiS4, and YBa2Cu3O6.5. The reaction network presents opportunities for enabling reaction pathway prediction, rapid iteration between experimental/theoretical results, and ultimately, control of the synthesis of solid-state materials.

Date: 2021
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DOI: 10.1038/s41467-021-23339-x

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