  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 (ReactCA) 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 rudimentary kinetic
effects, the effects of reactant particle spatial distribution, particle
melting and reaction atmosphere. It achieves conservation of mass using a
stochastic, asynchronous evolution rule and estimates reaction rates using
density functional theory data from the Materials Project [1] and machine
learning estimators for the the melting point [2] and the vibrational entropy
component of the Gibbs free energy [3]. 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
BaTiO$_3$, CaZrN$_2$ and YMnO$_3$ found in the literature to illustrate the
performance of the model in capturing reaction pathways as a function of
temperature, reaction selectivity and the effect of precursor choice. Our
approach allows for straightforward comparison of predicted mass fractions of
intermediates and products with experimental results. This simulation framework
presents a step toward $\textit{in silico}$ synthesis recipe design and an
easier way to optimize existing recipes, aid in the identification of
intermediates and identify effective recipes for yet unrealized inorganic
solids.
