What if we could build batteries for electric cars that would take us further than a full tank of gasoline? If we could grow affordable, tasty and nutritious meat in the laboratory instead of occupying one third of the land on our planet with animal farming? What if we could easily identify promising targets in the human body for new cancer drugs?
Underpinning all of these challenges is: chemistry. The challenge is not a scarcity of potential high-value compounds. Quite the opposite: The problem is that it just takes too long to test every possible compound for the desired chemical properties. Therefore, we must find ways to reliably predict these properties.
While quantum-chemical simulations allow us to accurately calculate chemical properties, their large computational cost as well as the huge number of molecules and materials make an exhaustive exploration infeasible. This talk introduces deep learning models for a variety of use cases in quantum chemistry. By analyzing the learned representations, we get a glimpse into the inner working of the neural network to find out whether the model has learned known chemical concepts – or has even uncovered hitherto unknown mechanisms!
The event took place on September 28th, 2018.
Find Kristof’s slides on SlideShare.
