“The flagship is designed as a ten-year vehicle,” he explains. “Our ambition is not just to continue doing excellent materials research, but to help shape what advanced materials research will look like ten years from now.”

AI and automation

Central to that vision is the integration of artificial intelligence and laboratory automation. Rather than relying solely on traditional trial-and-error experimentation, researchers increasingly use AI and automated workflows to accelerate the discovery and development of new materials.

“We believe AI and automation will fundamentally change how new materials are discovered,” Tito says. “Our goal is to embrace that paradigm and become one of Europe’s leaders in this area over the next decade.”

Unlike many universities, TU/e covers much of the full materials knowledge chain—from sustainable design, computational prototyping, and synthesis to engineering, processing, testing and implementation. 

That makes it possible, according to Tito, to connect data across disciplines in a way that is often not feasible elsewhere. The flagship is intended as an open platform rather than a fixed program, encouraging researchers across the university to contribute ideas and build new initiatives.

“We want this to be something the entire university can help shape,” Tito says. “By bringing together researchers from different departments and presenting a shared story externally, we can strengthen collaboration within TU/e while also increasing our visibility towards European funding agencies and other partners.”

“ChatGPT for materials research”

A key part of the flagship’s long-term strategy is the development of a TU/e Materials Hub, planned within the first five years. Rather than a physical center, it is envisioned as a virtual platform that brings together decades of materials research across the university.

“Almost like a ChatGPT-style tool,” Tito says. Researchers would be able to query the university’s collective knowledge base, receiving synthesized insights from years of published research. “This oracle should turn accumulated knowledge into answers—text, illustrations, tables, graphs—and hopefully even suggest new scientific directions.”

The concept addresses a broader challenge in materials science: research is often spread across different stages of the knowledge chain, from early design and synthesis to later processing, testing and application. Insights generated in one part of the process are not always visible to researchers working in another.

The goal of the Materials Hub is not only to map existing expertise, but to make it actively usable: a system that helps researchers identify connections across disciplines and reveal opportunities that might otherwise remain hidden in scattered publications and data.

Intelligent lab

Within ten years, the flagship also aims to develop an Intelligent Materials Lab. The concept builds on TU/e’s existing network of laboratories, which are spread across different buildings and each combine their own instruments and expertise. Rather than consolidating them into a single space—which Tito stresses would not be practical—the focus is on connectivity.

“One way to do that is by introducing automation into these laboratories—robots and processes that carry out experiments and measurements for you.” In this vision, these systems would be directly linked to an “AI core”, allowing experimental data to flow automatically into a central layer.

“One way to do that is by introducing automation into these laboratories—robots and processes that carry out experiments and measurements for you.” In this vision, these systems would be directly linked to an “AI core”, allowing experimental data to flow automatically into a central layer.

From there, AI could operate across the labs: analyzing data, generating ideas for new materials research, and potentially suggesting or triggering follow-up experiments. “But in this paradigm, human creativity and scientific expertise remains firmly at the head, in the driver’s seat,” says Tito.

Tito acknowledges the concept is ambitious, but sees it as a realistic trajectory. “It might sound a bit like science fiction,” he says, “but given the speed at which AI and automation are progressing, it’s not too unrealistic.”

Agility

The flagship’s ambitions are clear, but the path toward them is not fixed. Rather than following a detailed roadmap, the initiative is built around adaptability. “We have not set a super explicit month-by-month plan for ten years,” Tito explains. “We simply don’t know what the future will hold.”

That uncertainty is not seen as a weakness, but as a condition for progress. The coming years are likely to bring unexpected opportunities, shifts in direction, and necessary adjustments. “The path we take will be complicated and challenging,” Tito says. “It might change, it might take detours—but the 10-year goal is fixed, and achieving this requires agility.”

Asked what success would look like in ten years, Tito points not to a single breakthrough, but to broader recognition of TU/e’s research. “I would like to see the people of TU/e—researchers, students, and staff—in the scientific and national media, with our achievements in the spotlight.”

“Making an impact, for example through major grants to push the boundaries of AI and automation for advanced materials. The flagship is about people, challenging our community to do something new and exciting, and giving it international visibility.”