A Computational Workflow for Cost-Effective Synthesis of Inorganic Materials: Integrating Thermodynamics, Cellular Automata, Machine Learning, and Commercial Databases

This work presents a workflow designed to enhance economic efficiency in indus-trial processes by optimizing material synthesis through thermodynamic modeling,cellular automata, machine learning (ML), and commercial databases. The method-ology enables the systematic identification and evaluation of synthesis routes thatbalance thermodynamic performance with economic profitability. As a proof ofconcept, the workflow was applied to the production of Ca2SiO4, a key refrac-tory material for boilers and ship engines. Three novel synthesis recipes and fouroptimal commercial reagents (CaO, SiO2, Ca3SiO5, and Si3N4) were identified,demonstrating both improved process efficiency and cost-effectiveness. Beyondthis case study, the approach is broadly applicable to a wide range of inorganic sys-tems, offering a scalable path toward maximizing economic efficiency in materialproduction. These results highlight the potential of data-driven workflows to accel-erate the development of sustainable and competitive industrial manufacturing.