This abstract describes how materials informatics and applied machine learning are enabling faster reformulation in the coatings industry. As market, customer, and supply demands change rapidly, companies are moving beyond intuition-driven R&D toward data-driven development. Materials informatics captures historical experimental data along with expert domain knowledge, preserving and reusing it to guide future formulation decisions. By applying machine learning strategies, coatings developers can accelerate reformulation, reduce trial-and-error experimentation, and respond more quickly to evolving requirements. The paper outlines core concepts for successfully deploying materials informatics to improve speed, knowledge retention, and innovation in coatings product development.

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