A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions

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.