Machine learning represents an exciting opportunity to accelerate discovery in the chemical sciences, and to shorten the time from discovery to products. However, the available (experimental) data for chemistry is often limited, and it is not equally distributed in the vast “chemical space”. Our approach is try to bridge this gap by relying on a combination of machine learning and physical simulation.

In the first part of the talk, I will describe our work in the field of molecular design for organic electronic materials. In the second part of the talk, I will present our work in the area of reaction prediction, using a combination of quantum-chemical models and machine learning.

Kjell Jorner is an Assistant Professor of Digital Chemistry at ETH Zurich.

Location:  Zoom, or EDIT Analysen, Chalmers Campus Johanneberg

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