A collaborative team of researchers led by Lehigh University is pioneering new artificial intelligence (AI) techniques to revolutionize materials science. Their project, titled “ Harnessing Nonnegative Matrix Factorization for Advanced Computational Materials Modeling ,” is supported by an $800,000 grant from the U.S. Department of Energy. This research focuses on developing advanced scientific machine learning (SciML) algorithms to help scientists analyze vast amounts of complex material data. These cutting-edge AI tools will accelerate the discovery of new materials with applications in energy, manufacturing, and beyond.
Driving this project is a challenge that has long puzzled scientists — how can we accurately predict the properties of materials before they are even created? Traditional methods rely on trial-and-error experiments, which can be costly and time-consuming. By combining mathematical modeling with AI-powered learning, this research aims to decode the fundamental relationships between a material’s structure and its properties, enabling scientists to design new materials with specific functionalities – such as stronger, lighter, or more energy-efficient compounds — entirely in the digital space.
The research is led by Chinedu Ekuma, assistant professor of physics at Lehigh, and brings together experts in machine learning, physics and materials science. The team is developing a new class of interpretable AI models, ensuring that scientists can understand and trust the decisions made by machine learning algorithms.
The project is built on four major innovations:
By developing AI systems that are not only powerful but also explainable, this project marks a major step toward making AI a trusted tool in scientific discovery. The results of this research could accelerate innovations in material design, leading to advancements in:
The research team is comprised of: