Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing coatings formulations by optimizing product development, enhancing sustainability, and improving performance predictions. In looking at AI, it reminded me a lot of combinatorial chemistry from the likes of Accelerys (BIOVIA), Chemspeed and Symyx Technologies with high throughput experimentation. In 1996 or thereabouts, I was at Dow Chemical Corporation, where the techniques were successfully used to develop novel polyolefin catalysts from what used to take five years to less than 3. FastFormulator and Citrine Informatics are two of many AI Tools now available. There are different modules that are targeted for material combinations, manufacturing, compliance and regulation and intellectual property and patents.
In coatings, high throughput research was used by GE and North Dakota State University to reduce the iterations and number of formulations in product development. As stated above, there were other companies that used parts of combinatorial chemistry but North Dakota State was primarily the first entity that used extensive methods.
AI is used in many places and is growing in many applications. For the sake of this article the focus will be on a few areas where and how it is utilized in coatings.
Applications of AI in Coatings
Optimization of Formulations:
AI and ML enable formulators to analyze enormous datasets and identify optimal ingredient combinations. Using traditional reformulation efforts are very time and resource intensive. This significantly reduces the time and resources needed for traditional trial-and-error methods. For instance, AI can predict the performance of different resin formulations before physical testing, allowing for more effective R&D cycles.
Predictive Modeling:
Machine learning models can estimate how different formulations will behave under various conditions. This capability allows chemists to construct new coatings with desired properties, such as durability, adhesion and weathering without extensive physical experimentation. AI-powered computer vision systems monitor production lines in real-time, detecting defects and inconsistencies to ensure high-quality paint production. AI predicts equipment failures before they occur, allowing for proactive repairs and minimizing downtime.
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Sustainability and Regulatory Compliance:
AI helps companies meet sustainability goals by optimizing formulations to replace harmful substances and reduce environmental impact. For example, AI-driven approaches have been used to substitute restricted ingredients while maintaining product performance. There are a multitude of toxic or suspect chemicals they have been used as additives which are now in question. AI helps substitute materials.
Augmented R&D Cycles:
By leveraging AI, companies can condense the development time for new coatings from several months to just a few weeks. This responsiveness allows manufacturers to react quickly to market demands and regulatory changes. AI optimizes resource allocation, ensuring minimal waste and energy consumption during production, as well as differentiated solutions.
Enhanced Data Utilization:
AI systems can learn from both large datasets and incremental data inputs, similar to how researchers adjust their formulations based on experimental results. This continuous learning process improves the accuracy and effectiveness of the formulations over time. AI enables companies to offer personalized solutions to their customers by analyzing data and predicting customer preferences.
Tangible Applications:
At the core of AI technology lies the ability to analyze vast amounts of data, recognize patterns, and make conclusions at a speed and scale beyond human abilities. These capabilities are already being used to enable more immersive, personalized experiences and help individuals and enterprises unlock new levels of creativity, discovery, and efficiency. There is a universal truth: no AI initiative succeeds without a strong data organization. But what that foundation looks like varies from company to company, and market to market.
Initial work with various AI and ML platforms met with frustration with inaccessible lab data. Efficiency came from simply having everything accessible. That foundation is what makes AI valuable today.
Another problem was the overuse and expectation that everything would be pristine. Perfection is the enemy of progress and if you wait for the perfect dataset, you’ll never start.
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Customer Experiences
AI-powered chatbots are helping customers across industries get their questions answered more quickly and resolve issues more easily. Computer vision systems are being used to enable frictionless, touchless checkout, optimize store layouts based on observed customer traffic patterns, and enable AI-augmented driving capabilities.
Polyurea Coatings:
AI is particularly beneficial in formulating polyurea systems, known for their robustness and rapid curing. AI-driven methods can optimize the balance of properties like toughness and adhesion, making the formulation process more efficient.
Sustainable Ink Formulations:
Companies are using AI to develop biodegradable ink formulations, focusing on properties such as viscosity and flow to ensure quality and performance.
PFAS-free adhesives:
An adhesives and sealants company needed to retain its innovation edge while observing new PFAS regulations and shifting demands. The goal was to reformulate a pressure-sensitive adhesive to exclude PFAS without compromising performance. By using the Citrine Platform, they created a simulation within three months to test small molecules and assess millions of ingredient combinations. This led to the identification of several possible candidates. In less than four months a candidate was identified, reducing the projected time to market from five years to two.
AI’s Transformative Impact on Coatings
The AI revolution in the coatings industry is a transformative journey that has already made a significant impact. Here are some key points highlighting the AI’s role in the coatings industry:
AI-powered computer vision systems have changed the game. These systems use high-resolution cameras and machine learning algorithms to monitor the production line in real time. They can detect microscopic defects, inconsistencies in viscosity, or slight color deviations instantly.
By catching these issues early, manufacturers can make immediate adjustments, ensuring every batch meets exact specifications. This not only improves the final product but also reduces the costs associated with subpar batches that must be reprocessed or discarded.
In summary, AI and ML are transforming the coatings industry by enhancing formulation processes, improving sustainability, and accelerating product development, making them invaluable tools for modern chemists and engineers.
Resources:
- Leveraging AI and Machine Learning in Coatings, Adhesives, and Sealants
- Bing Videos (AI Product Development)
- Digital advancements in smart materials design and multifunctional coating manufacturing – ScienceDirect
- 11 real-world agentic AI examples and use cases | TechTarget
- Using AI to Rapidly Develop New and Improved High-performance Coatings
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) as the applications and development manager in Core R&D in the Coatings & Functional Polymers Group. He also managed the TS&D group for coatings while at Dow Chemical (1995-99) and held positions at Rhodia (Laboratory Manager, Latex & Specialty Polymers (1989-95)) and was the Development Chemist, exterior latex paints at Benjamin Moore & Co. (1979-89).