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BASF Cooperates with TU Berlin to Advance Artificial Intelligence

| Editor: Alexander Stark

Machine learning is a key pillar of artificial intelligence.
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Machine learning is a key pillar of artificial intelligence. (Source: CC0 / Pixabay)

BASF and Technical University of Berlin (TU Berlin) have signed an agreement to cooperate closely in the area of machine learning. The aim of the collaboration is to develop workable new mathematical models and algorithms for fundamental questions relating to chemistry, for example, from process or quantum chemistry.

Ludwigshafen/Germany; Berlin/Germany — BASF and the TU Berlin announced their joint commitment to advance Artificial Intelligence in the coming years. As essential part of the cooperation, the company supports the research work of Prof. Dr. Klaus Robert Müller, professor for machine learning and spokesperson of the “Berlin Center for Machine Learning” at the TU Berlin, with about $ 2.8 million over the next five years.

With machine learning, large volumes of data are analyzed to recognize patterns and relationships which can be used to develop prediction models that optimize themselves based on their results. Systems for language recognition or autonomous driving are examples of how machine learning is used in day-to-day applications. Ultimately, the mathematical models in these everyday examples are similar to those needed in a digitalized laboratory, explains Dr. Hergen Schultze, head of BASF’s research group “Machine Learning and Artificial Intelligence.”

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Since there was no off-the-shelf software for machine learning, the goal of the project was to develop new basic principles of machine learning for very specific applications in research, says Dr. Bruno Betoni, who is responsible for Baslearn, (Berlin-based Joint Lab for Machine Learning) at the chemical company. He added that TU Berlin had a wealth of expertise in this area. He is convinced that this cooperation will help both partners make important progress. With this cooperation, the company gets access to huge volumes of real, highly complex data, which they intend to use to develop new algorithms.

The application areas for machine learning range from biological systems and research on materials and active ingredients to laboratory automation and dynamic process systems. The joint research work will investigate issues such as the solubility of complex mixtures or dyes as well as predicting the aging process of catalysts.

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