A Cellular\nAutomaton Simulation for Predicting Phase\nEvolution in Solid-State Reactions

New computational tools for solid-state synthesis recipe\ndesign\nare needed in order to accelerate the experimental realization of\nnovel functional materials proposed by high-throughput materials discovery\nworkflows. This work contributes a cellular automaton simulation framework\nfor predicting the time-dependent evolution of intermediate and product\nphases during solid-state reactions as a function of precursor choice\nand amount, reaction atmosphere, and heating profile. The simulation\ncaptures the effects of reactant particle spatial distribution, particle\nmelting, and reaction atmosphere. Reaction rates based on rudimentary\nkinetics are estimated using density functional theory data from the\nMaterials Project and machine learning estimators for the melting\npoint and the vibrational entropy component of the Gibbs free energy.\nThe resulting simulation framework allows for the prediction of the\nlikely outcome of a reaction recipe before any experiments are performed.\nWe analyze five experimental solid-state recipes for BaTiO<sub>3</sub>, CaZrN<sub>2</sub>, and YMnO<sub>3</sub> found in the literature\nto illustrate the performance of the model in capturing reaction selectivity\nand reaction pathways as a function of temperature and precursor choice.\nThis simulation framework offers an easier way to optimize existing\nrecipes, aid in the identification of intermediates, and design effective\nrecipes for yet unrealized inorganic solids <i>in silico</i>.