Artificial intelligence and machine learning are transforming the coatings industry by optimizing complex formulations, meeting regulatory demands, and speeding up research and development. One notable example is a global construction chemicals company that used an AI platform to optimize a multi-layer coating system, reducing the development timeline by over 50%. This approach combined domain expertise with machine learning to model each layer and the system as a whole.
The coatings industry faces challenges such as reacting quickly to changing customer demands while balancing cost and performance, meeting sustainability goals, and redesigning existing formulations to replace restricted substances. AI platforms provide a strategic tool to shorten development cycles, support regulatory compliance, and preserve critical know-how. For instance, an industrial minerals company optimized its production line settings through AI models, cutting overall manufacturing costs by 20% while reducing energy use and carbon footprint.
Moreover, AI-driven approaches can significantly shorten the development cycle of new coating systems. A global manufacturer in the adhesives and sealants sector used AI to reformulate a pressure-sensitive adhesive, eliminating PFAS while maintaining high performance. The AI platform screened millions of small molecules and identified promising candidates, reducing the development time from five years to just two months.
The future of coatings innovation looks promising with AI and machine learning playing a major role. Companies are using AI to create new colors, predict performance, and enhance sustainability. For example, a paint company launched an AI-powered voice recognition tool that allows people to utter relevant phrases to find the best paint options. AI is also being used to develop coatings that slow corrosive effects, create transparent radiative coolers to block near-infrared light and ultraviolet rays, and design new coatings to support emerging needs.
