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

McDermott, Matthew J.; Dwaraknath, Shyam S.; Persson, Kristin A.

doi:10.1038/s41467-021-23339-x

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

Journal Article · 24 May 2021 · Nature Communications

DOI: https://doi.org/10.1038/s41467-021-23339-x · OSTI ID:1784578

McDermott, Matthew J.;

;

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 YMnO ₃ , Y ₂ Mn ₂ O ₇ , Fe ₂ SiS ₄ , and YBa ₂ Cu ₃ O _6.5 . 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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Stony Brook Univ., NY (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-05CH11231; SC0019212

OSTI ID:: 1784578

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 12; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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

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