McDermott Matthew J, Dwaraknath Shyam S, Persson Kristin A
Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Department of Materials Science and Engineering, University of California, Berkeley, CA, USA.
Nat Commun. 2021 May 25;12(1):3097. doi: 10.1038/s41467-021-23339-x.
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, YMnO, FeSiS, and YBaCuO. 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.
在寻找新型功能材料的过程中，加速无机合成仍然是一项重大挑战。尽管有大量的计算/实验热化学数据，但在固态化学中，许多能实现合成有机化学中“设计合成”的原理并不存在。在这项工作中，我们基于现有的热化学数据构建了一个用于固态合成的化学反应网络模型，并设计了一种计算上易于处理的方法，通过应用路径查找算法和网络中最低成本路径的线性组合来推测可能的反应途径。我们展示了该网络在预测与文献报道的YMnO、YMnO、FeSiS和YBaCuO相当的复杂反应途径方面取得的初步成功。该反应网络为实现反应途径预测、实验/理论结果之间的快速迭代以及最终控制固态材料的合成提供了机会。
