# Computational Materials Synthesis plus AI Expansion

Computational materials synthesis accelerates material discovery by using simulations and data-driven methods. It models chemical reactions and kinetics, predicting synthesis routes and optimizing conditions. Atomistic simulations, like MD and KMC, reveal atomic-level material formation. DFT calculations predict material properties, aiding in screening. Materials informatics and databases, such as MP, enable machine learning for trend identification. Computational tools also guide reactant selection. This approach integrates modeling, informatics, and experiments, enhancing the design and development of materials for diverse applications.

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