Rapid Materials and Process Development

Traditionally, the complete materials development process can take decades, relying on trial-and-error approaches based on literature, experience, or researchers’ intuition. However, the path to new materials is highly complex and multidimensional, requiring the consideration of variables such as composition, processing, and post-treatment. Reducing development time by at least 50% is therefore critical to addressing current societal, economic, and geopolitical challenges driven by climate change, resource scarcity, and competition over strategic markets.

At our institute, research in this area focuses on four key themes:

Rapid Materials Simulation

We apply, adapt, and advance high-throughput experimental and simulation methods combined with AI techniques to efficiently explore vast design spaces. Quantum computing is also being investigated to further improve the speed and precision of material simulations.

Rapid Materials Production

Once promising candidates are identified through simulations, high-throughput screening and combinatorial approaches are used for rapid experimental synthesis. We extend this sequential process through AI integration and robotic systems, enabling autonomous manufacturing platforms that unify virtual screening, candidate identification, and prototype production.

Rapid Materials Analytics

Modern multiscale, multimodal, and high-resolution analysis tools are producing unprecedented volumes of data. By combining imaging techniques—microscopic (2D), tomographic (3D), and time-resolved—with AI methods such as machine learning, and even foundation models, we dramatically reduce analysis and interpretation times, accelerating the entire development chain.

Rapid Materials Testing

To speed up materials testing, we pursue two approaches:

  1. Exploratory Testing: Robotic systems autonomously prepare and test samples, for example, directly from manufacturing processes, to identify property trends and design spaces.
  2. High-Fidelity Testing: In later stages of development, advanced testing combines physical methods, AI, robotics, simulations, and immersive environments such as augmented reality. These autonomous or interactive systems aim to minimize the number of required tests through intelligent prediction and optimization.