New computational tools for solid-state synthesis recipe design are needed in order to accelerate the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. This work contributes a cellular automaton simulation framework for predicting the time-dependent evolution of intermediate and product phases during solid-state reactions as a function of precursor choice and amount, reaction atmosphere, and heating profile. The simulation captures the effects of reactant particle spatial distribution, particle melting, and reaction atmosphere. Reaction rates based on rudimentary kinetics are estimated using density functional theory data from the Materials Project and machine learning estimators for the melting point and the vibrational entropy component of the Gibbs free energy. The resulting simulation framework allows for the prediction of the likely outcome of a reaction recipe before any experiments are performed. We analyze five experimental solid-state recipes for BaTiO₃, CaZrN₂, and YMnO₃ found in the literature to illustrate the performance of the model in capturing reaction selectivity and reaction pathways as a function of temperature and precursor choice. This simulation framework offers an easier way to optimize existing recipes, aid in the identification of intermediates, and design effective recipes for yet unrealized inorganic solids in silico.

中文翻译：

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用于预测固态反应中相演化的元胞自动机模拟


需要用于固态合成配方设计的新计算工具，以加速高通量材料发现工作流程提出的新型功能材料的实验实现。这项工作提供了一个元胞自动机模拟框架，用于预测固态反应过程中中间相和产物相的时间依赖性演变，作为前驱体选择和量、反应气氛和加热曲线的函数。仿真捕获了反应物粒子空间分布、粒子熔融和反应气氛的影响。使用来自材料项目的密度泛函理论数据和吉布斯自由能的熔点和振动熵分量的机器学习估计器，估计基于基本动力学的反应速率。由此产生的模拟框架允许在进行任何实验之前预测反应配方的可能结果。我们分析了文献中发现的 BaTiO ₃ 、 CaZrN ₂ 和 YMnO ₃ 的 五 种 实验 固态 配方，以说明该模型在捕获反应选择性和反应途径与温度和前驱体选择的关系方面的性能。该模拟框架提供了一种更简单的方法来优化现有配方，帮助识别中间体，并为尚未实现的计算机无机固体设计有效的配方。