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

Area of Science:

• Materials Science
• Computational Chemistry
• Solid-State Chemistry

Background:

• High-throughput materials discovery workflows require efficient computational tools for solid-state synthesis recipe design.
• Accelerating the experimental realization of novel functional materials is crucial for materials innovation.

Purpose of the Study:

• To develop a cellular automaton simulation framework for predicting the time-dependent evolution of phases during solid-state reactions.
• To enable in silico design and optimization of solid-state synthesis recipes.

Main Methods:

• A cellular automaton simulation framework was developed to model solid-state reactions.
• Reaction rates were estimated using density functional theory data and machine learning models for melting point and Gibbs free energy.
• The simulation incorporates reactant particle distribution, melting, and reaction atmosphere effects.

Main Results:

• The framework predicts the likely outcome of a reaction recipe before experimental synthesis.
• Analysis of five experimental recipes for BaTiO3, CaZrN2, and YMnO3 demonstrated the model's ability to capture reaction selectivity and pathways.
• The simulation accurately predicted reaction outcomes based on temperature and precursor choice.

Conclusions:

• The developed simulation framework facilitates the optimization of existing recipes and the design of new recipes for inorganic solids.
• This tool aids in identifying reaction intermediates and accelerates the discovery of novel functional materials.
• The computational approach offers a significant advancement for in silico materials design and synthesis planning.