Predicting Phase Evolution During Solid-State Synthesis Using Cellular Automata

Computational tools, like the selectivity metrics from [1] for solid-state synthesis recipe design can significantly aid in the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. With the emergent setting of the autonomous laboratory in mind, we present a new simulation framework and digital twin for predicting solid-state synthesis reaction outcomes. This framework uses the cellular automaton formalism [2] to predict 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. Our framework incorporates high-throughput thermodynamic data, machine-learning estimators for melting points and finite temperature Gibbs formation energies, and empirical heuristics to estimate reaction rates. These rates are then utilized in repeated applications of an evolution rule to produce a trajectory which can be analyzed to uncover reaction pathways and intermediates, thereby enabling the synthesis scientist to test their recipe <i>in silico</i>. We analyze seven experimental case study recipes from the literature detailing the synthesis of BaTiO₃ and YMnO₃ to illustrate the predictive power of this model in capturing reaction selectivity, reaction onset temperature dependence, the effect of precursor choice, and recovery of the experimental reaction pathway. Finally, we share our vision for the use of this model in the autonomous laboratory [3] and the optimization framework, emphasizing its capacity both to optimize existing recipes and to accelerate the identification of effective recipes for yet unrealized solid materials.<br/><br/><br/>References<br/>[1] M. J. McDermott et al., “Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials,” ACS Cent. Sci., vol. 9, no. 10, pp. 1957–1975, Oct. 2023, doi: 10.1021/acscentsci.3c01051.<br/>[2] M. C. Gallant and K. A. Persson, “pylattica: a package for prototyping lattice models in chemistry and materials science,” Journal of Open Source Software, vol. 9, no. 97, p. 6170, May 2024, doi: 10.21105/joss.06170.<br/>[3] N. J. Szymanski et al., “An autonomous laboratory for the accelerated synthesis of novel materials,” Nature, vol. 624, no. 7990, Art. no. 7990, Dec. 2023, doi: 10.1038/s41586-023-06734-w.