Materials Informatics is used across products, materials and chemicals, anywhere where the properties of a substance give competitive advantage. Material informatics cuts through complexity. Today’s challenges are a complex balance of competing demands, and the experimental data used to find solutions are complicated, subtle and expensive. Informatics uses computational techniques and statistics to analyze and interpret this data far more efficiently than a human could. AI models help us understand trends and relationships in our data, and suggest recipes to achieve our goals. To create innovative, high-performance materials, traditionally scientists have needed to test many ideas physically. Materials Informatics gets AI to run many, many virtual experiments, so they can refine their theories and focus on the most promising options. Materials informatics lets us efficiently discover, develop and produce new products, materials and chemicals. New products can take years to develop, using specialized knowledge, trial-and-error, and luck. Materials informatics accelerates this. By accelerating innovation, product experts can be more agile in responding to customer requirements, production issues, supply shocks and regulation. As a society, we can expedite the science we need to solve our biggest challenges.
