AID4GREENEST research was featured at AIMET 2026 (Conference on AI in Metallurgy) in Mechelen, Belgium, where project partners presented innovative work on the use of generative artificial intelligence for materials science and steel development.
The presentation highlighted research by Dr. Daria M. Tomecka, carried out within the AID4GREENEST project, exploring how generative AI can be used to create realistic synthetic microstructures to supplement experimental materials datasets. By generating data in areas where experimental information is limited, these models can help overcome one of the major challenges in materials development: data scarcity.
Beyond filling gaps in existing datasets, the technology opens the door to virtual experimentation, enabling researchers to predict the microstructures and properties resulting from specific processing routes before a steel is ever manufactured. Such capabilities have the potential to accelerate materials development while reducing the time and resources required for experimental testing.
The presentation also addressed an important topic at the intersection of artificial intelligence and scientific research: trustworthiness and data provenance. As synthetic microstructures become increasingly difficult to distinguish from experimentally acquired data, robust validation procedures, transparency, and clear documentation of data origins become essential. While synthetic data offers significant opportunities to enhance research, its responsible use is critical to maintaining scientific integrity.
This work, developed through collaboration between Ghent University and ePotentia within the AID4GREENEST consortium, demonstrates how advanced AI technologies can support the development of greener, more efficient steel manufacturing processes while highlighting the importance of responsible innovation.
The presentation formed part of the project’s ongoing dissemination activities, showcasing the growing role of artificial intelligence in accelerating materials discovery and characterisation for sustainable steel technologies.