Professor Maurer is revolutionizing chemistry with artificial intelligence!

Professor Maurer is revolutionizing chemistry with artificial intelligence!

With the nomination of Prof. Dr. Reinhard Maurer's Alexander von Humboldt Professorship brings an innovative player in the world of theoretical chemistry into the spotlight. This nomination was made by the University of Göttingen and the Max Planck Institute for Multidisciplinary Natural Sciences (MPI-NAT). The professorship, which is endowed with five million euros over five years, is financed by the Federal Ministry of Research, Technology and Space. Maurer is a pioneer in the application of machine learning (ML) and artificial intelligence (AI) in this field and will use his expertise to make significant advances in computational materials research.

Maurer's research interests focus on the theory and simulation of molecular reactions on surfaces and in materials. He opens up promising possibilities by developing a new approach that uses deep learning to predict experiment results. Its algorithm enables the inverse design of molecular structures that exhibit specific chemical properties. This methodology not only has an impact on chemistry, but is also transferable to other disciplines, which illustrates the interdisciplinarity of his research.

How machine learning is transforming research

Machine learning is considered a key technology in the field of artificial intelligence. According to a study by the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, ML can contribute significantly to global economic development. It is transforming various areas such as goods production, logistics and even medical technology. The ability of AI models to process large amounts of data and recognize patterns is becoming increasingly important. Even in mathematics, optimization problems are already being used to adapt and improve models, which illustrates the interplay between theory and application.

Suvrit Sra, the professor of “Resource Aware Machine Learning” at the Technical University of Munich (TUM), will play a significant role in ML research in the coming years. His focus is on the robustness, reliability and resource efficiency of ML methods. TUM's existing leading position in the field of artificial intelligence in Germany will be further consolidated through Sra's work. The cooperation with the computer scientist Stefanie Jegelka, who also holds a Humboldt Professorship at TUM, will have a lasting impact on developments in the field of ML.

Social acceptance through education

A central concern of the current debates is the need for a fact-based discussion of AI and ML technologies. The public discourse is often characterized by half-knowledge and myths, which hinders society's acceptance. Comprehensive education about ML and its applications is essential to promote trust and understanding of these technologies. The study “Machine Learning – Skills, Applications and Research Needs” provides a valuable basis for this and highlights various aspects of research that are crucial for the positioning of Germany and Europe in international competition.

The developments in science and technology, as driven by Maurer's nomination for the Humboldt Professorship and the initiatives of Sra and Jegelka, are promising. They show how interdisciplinary approaches can drive progress in modern science and society.