Abstract:

Machine learning is emerging as a powerful tool in quantum chemistry and materials science, combining the accuracy of electronic structure methods with computational efficiency. Going beyond the simple prediction of chemical properties, machine learning potentials can be applied to perform fast molecular dynamics simulations, model solvent effects and response properties as well as find structures with desired properties by inverse design. In this talk, we will show how this opens a clear path towards unifying machine learning and quantum chemistry.

Biographies:

Klaus-Robert Müller has been a professor of computer science at Technische Universit{\"a}t Berlin since 2006; at the same time he is co-directing the Berlin Big Data Center. He studied physics in Karlsruhe from 1984 to 1989 and obtained his Ph.D. degree in computer science at Technische Universit{\"a}t Karlsruhe in 1992. After completing a postdoctoral position at GMD FIRST in Berlin, he was a research fellow at the University of Tokyo from 1994 to 1995. In 1995, he founded the Intelligent Data Analysis group at GMD-FIRST (later Fraunhofer FIRST) and directed it until 2008. From 1999 to 2006, he was a professor at the University of Potsdam. He was awarded the Olympus Prize for Pattern Recognition (1999), the SEL Alcatel Communication Award (2006), the Science Prize of Berlin by the Governing Mayor of Berlin (2014), and the Vodafone Innovations Award (2017). In 2012, he was elected member of the German National Academy of Sciences-Leopoldina, in 2017 of the Berlin Brandenburg Academy of Sciences and also in 2017 external scientific member of the Max Planck Society. In 2019 and 2020 he became ISI Highly Cited Researcher. His research interests are intelligent data analysis and machine learning with applications in neuroscience (specifically brain-computer interfaces), physics and chemistry.

Kristof T. Schütt is a senior researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He received his master's degree in computer science in 2012 and his PhD in machine learning in 2018 at the machine learning group of Technische Universität Berlin. Until September 2020, he worked at the Audatic company developing neural networks for real-time speech enhancement. His research interests include interpretable neural networks, representation learning, generative models, and machine learning applications in quantum chemistry.