The invention makes bold yet practical claims, offering reliable predictions of multiple material properties with built-in safeguards against impossible results.
A groundbreaking collaboration between Oreoluwa Alade, a PhD candidate in Computational Physics at North Dakota State University, and Onuh Matthew Ijiga, an Applied Physicist and Data Analyst at Joseph Sarwuan Tarka University, has achieved official recognition in the United Kingdom. Their joint invention—a Predictive AI Graphical User Interface (GUI) for Polymer Thermodynamics—has been successfully registered under Design No. 6453099 by the UK Intellectual Property Office. This invention brings artificial intelligence and physics together in a way that could reshape industries that depend on advanced materials.
At its core, the tool is designed to predict how polymers (a type of material used in plastics, packaging, aerospace, and healthcare) behave under different conditions. By blending AI with established laws of physics, the platform provides real-time forecasts of critical properties such as heat capacity, energy storage, and material stability. Unlike traditional trial-and-error laboratory testing or “black-box” AI tools, this system ensures that its predictions make scientific sense—saving time, reducing costs, and building trust in the results. “One of our biggest goals was to build a platform that doesn’t just give answers, but gives answers that align with real science,” Oreoluwa explained. “That was the breakthrough—integrating physics constraints into AI.”
The system works much like an interactive dashboard. Users simply enter basic details about a polymer—such as its type, weight, temperature, or pressure—and the software generates instant visual reports. These reports highlight how the material will respond to heat, pressure, or chemical changes. This is vital for industries like aerospace, where polymers must withstand extreme conditions, or in pharmaceuticals, where material safety and stability are critical. “We wanted the interface to be intuitive,” Onuh added. “Whether you’re an engineer in a factory or a researcher in a lab, you can use it without needing to be an AI specialist.”
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The invention makes bold yet practical claims, offering reliable predictions of multiple material properties with built-in safeguards against impossible results. It is flexible enough to adapt to new and emerging materials while maintaining transparency by providing clear explanations and confidence levels alongside every prediction, ensuring accuracy and trust. “Transparency was non-negotiable,” Oreoluwa said. “Every output comes with confidence levels and explanations so users know why a result is what it is.”
What makes this invention particularly exciting is how it draws from the researchers’ own ongoing work. Oreoluwa’s expertise in machine learning and phase transitions in microgels provided the AI backbone of the system, showing how advanced algorithms can achieve near-perfect accuracy in predicting complex behaviors. Meanwhile, Onuh’s research into green nanomaterials for wastewater treatment inspired the sustainability side of the platform. “I’ve always been focused on green solutions,” Onuh explained. “Linking that into a tool that helps design recyclable and eco-friendly polymers felt natural.” Together, they built a system that not only predicts but also supports the development of sustainable materials, reducing waste and supporting a circular economy.
The benefits are easy to imagine. A packaging company developing biodegradable plastics could quickly test how their product would hold up under different storage conditions—without expensive and lengthy lab experiments. Aerospace engineers designing high-performance materials could instantly check for weak points under extreme stress, avoiding catastrophic failures. And in environmental engineering, the tool could help scientists select the best polymers for water treatment membranes, building directly on Onuh’s research into photocatalytic nanomaterials. “When I saw how our combined expertise could touch packaging, aerospace, and even clean water solutions, it really brought home the importance of this invention,” Oreoluwa remarked.
The bigger picture is that this invention could transform how industries design and test materials. Instead of months of laboratory work, predictions are available in seconds. It cuts costs, speeds up innovation, and supports greener, more sustainable products. As manufacturing increasingly moves toward digital twin technology—where virtual models are used to simulate and optimize real-world production, this design is perfectly positioned to play a central role. “This is more than software; it’s a bridge to Industry 4.0,” Onuh emphasized. “It makes materials research faster, safer, and cleaner.”
The registration of Design No. 6453099 is more than just a legal milestone. It is a recognition of a bold new direction where artificial intelligence and physics meet to drive safer, faster, and more sustainable innovations. This achievement stands as a milestone not only for the inventors but for the many industries that rely on polymers every day—from the packaging that wraps our food to the high-performance materials that send spacecraft into orbit. “For us, the registration validates years of hard work,” Oreoluwa concluded. “But for industry, it’s a door to designing materials smarter and greener than ever before.”
