The continuous geographic and demographic expansion of humankind has relied on the assumption of essentially unlimited resources and particularly on the massive exploitation of fossil fuels. This has set us on a path toward a rapidly deteriorating environment and an impending age of scarcity, which will challenge the very fundamentals of nearly all production technologies. Accordingly, various research efforts are now focused on achieving a more sustainable, efficient and automated production.

The future will be renewable! Shaping a post-fossil era mandates the development of disruptive technologies for the production and use of liquid energy carriers and chemical products as basis for a truly sustainable energy-chemistry nexus within the planetary boundaries. Energyrich molecules harnessing renewable energy jointly with renewable material feedstocks offer an important contribution to the defossilization of the transport sector. This applies especially to long-haul, heavy duty, and non-road applications, which are difficult or even impossible to electrify, yet have significant contributions to the total energy demand. At the same time, energyrich molecules are essential components for a net-zero production of chemicals serving as the foundation for nutrition, health, and prosperity.

Since 2019, researchers from the Cluster of Excellence "Data-Integrated Simulation Science (SimTech)" at the University of Stuttgart have been developing a new class of simulation- and data-driven approaches that enhance the applicability and accuracy of simulations, changing the way we conduct science and technology.

The cluster "Sustainable and Energy-Efficient Aviation" seeks to establish scientific foundations for the transformation of the air transport system over the next decades.

The Cluster of Excellence "Science of Intelligence (SCIoI)" fundamentally advances our understanding of intelligence, integrate and unify theories, concepts, and insights from existing intelligence-related disciplines, catalyze progress in these disciplines, and - perhaps most importantly - advance our ability to construct intelligent technological artifacts for applications of societal importance.

“Responsible Electronics in the Climate Change Era” (REC²) will create disruptive paradigm shifts in the conceptualisation, design, realisation, usage and end-of-life treatment of electronic devices. Our society is dependent on electronics, which are increasingly integrated into every aspect of our lives. Electronics are essential for our continued progress, providing solutions to global challenges like climate change. At the same time, electronics are also part of the problem: Their already vast energy needs continue to grow, and their ever-shorter replacement cycles drive enormous consumption.

Over the past decade, deep learning (DL) has driven groundbreaking advances in artificial intelligence (AI). However, current DL-based AI systems are unreasonable in many ways: (1) They are developed and deployed in unreasonable ways, requiring overly large models, vast amounts of data and computational power, and extensive infrastructure. This has led to a monopoly held by a few large companies with the necessary resources. (2) They struggle with reasoning, handling unfamiliar situations, and nuanced context. They lack commonsense understanding and abstraction capabilities. (3) They do not improve continuously, learn through interactions, or adapt quickly. Instead, they require frequent retraining, resulting in high economic and environmental costs. Such unreasonable learning makes them brittle.

PhoenixD, unique in Germany for technical optics, pursues a holistic approach by developing innovative optical systems that integrate hardware and software.

Machine learning is changing science even more profoundly than we imagined only a few years ago. While it has proven useful in tackling individual, long-standing scientific problems, recent developments suggest even more far-reaching possibilities. Foundation models, trained on vast datasets, provide general purpose representations suitable for a wide range of downstream tasks, and have already revolutionized tasks involving language. Diffusion models allow the generation of samples from complex probability distributions, and modern programming frameworks make it possible to implement scientific theories as part of machine learning workflows. Thus, machine learning is creating new possibilities in nearly all stages of scientific discovery and inquiry. At the same time, machine learning methodologies have obvious shortcomings regarding reliability, robustness, and interpretability, and come with risks, challenges, and blind spots.

The vision of the livMatS-Cluster is to merge the best of two worlds, the biological and the tech-nological realm, to develop living adaptive energy-autonomous Materials Systems.