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Tuesday, 12 November 2019
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Markets & companies, Coatings market

BASF and TU Berlin launch artificial intelligence cooperation

Tuesday, 10 September 2019

BASF and Technische Universität Berlin (TU Berlin) have signed an agreement to cooperate closely in the area of machine learning.

BASF and the Technische Universität Berlin jointly develop workable new mathematical models and algorithms for fundamental questions relating to chemistry, for example, from process or quantum chemistry. Source: geralt / Pixabay.
BASF and the Technische Universität Berlin jointly develop workable new mathematical models and algorithms for fundamental questions relating to ch...

The aim of the collaboration, Berlin-based Joint Lab for Machine Learning (BASLEARN), is to develop workable new mathematical models and algorithms for fundamental questions relating to chemistry, for example, from process or quantum chemistry. Both partners are jointly committed to this aim in the coming years. As essential part of the cooperation, BASF 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 a total of over € 2.5 million over the coming five years.

Investigation of solubility of dyes

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. "This may not sound very complicated at first, but unfortunately it is. For example, we know the solubility of individual materials and simple mixtures. However, when there are several components in a formulation it is a different story,” says Dr. Hergen Schultze, head of BASF’s research group "Machine Learning and Artificial Intelligence.”

"The more data we use and the better adapted a learning model is, the better it can predict. In turn, our work in the lab becomes more efficient and together we reach our goal more quickly,” says Schultze. "Mathematical models can of course also control laboratory robots and thus carry out experiments,” adds Schultze, citing another application example. Robots could thus take over routine tasks or dealing with hazardous materials, for example, during reactor cleaning.

Image source: Pixabay

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