BASF and TU Berlin to advance artificial intelligence through BASLEARN

German chemical producer BASF and Technische Universität Berlin (TU Berlin) have partnered to cooperate in the area of machine learning through the Joint Lab for Machine Learning (BASLEARN) to be based in Berlin, Germany.

BASLEARN will focus on developing workable new mathematical models and algorithms for fundamental questions relating to chemistry in the coming years.

Dr. Bruno Betoni – responsible for BASLEARN at BASF said: “There is no off-the-shelf software for machine learning.

“Our goal is to develop new basic principles of machine learning for very specific applications in research. TU Berlin has a wealth of expertise in this area.”

BASF and TU Berlin partner to advance artificial intelligence through BASLEARN. Photo courtesy of Gewetz/Wikimedia Commons.

With a total of over €2.5 million over the next five years, BASF will support the research work of Dr. Klaus Robert Muller, professor for machine learning and spokesperson of the Berlin Center for Machine Learning at the TU Berlin.

Commenting on the joint research work, Dr. Klaus Robert Muller said: “Through this cooperation, we get access to huge volumes of real, highly complex data, which we can use to develop new algorithms.

“The scientific questions being investigated by BASF are extremely interesting and diverse.

“Such real-life challenges create very exciting and novel research questions regarding machine learning that theoreticians sitting at their desks would rarely come up with.”

BASLEARN will also investigate issues such as the solubility of complex mixtures or dyes as well as predicting the aging process of catalysts.

Dr. Hergen Schultze – head of BASF’s research group Machine Learning and Artificial Intelligence said: “Ultimately, the mathematical models in these everyday examples are similar to those needed in a digitalized laboratory.

“Mathematical models can of course also control laboratory robots and thus carry out experiments.”

Machine learning, which is a key pillar of artificial intelligence, will analyze large volumes of data to identify patterns and relationships for developing prediction models. Its application areas range from biological systems and research on materials and active ingredients to laboratory automation and dynamic process systems.