Unifying Machine Learning and Quantum Chemistry with Deep Neural Networks (Kristof Schütt)

Deep neural networks are emerging as a powerful tool in materials science and quantum chemistry, combining the accuracy of electronic structure methods with computational efficiency. Going beyond the simple prediction of chemical properties, neural network potentials can be applied to perform fast molecular dynamics simulations including solvent effects, model response properties and generate novel structures with desired properties. On several examples, I will demonstrate how this opens a clear path towards increased synergy of machine learning and quantum chemistry as well as designing workflows tightly integrated with experiment. This talk is part of the Discussion meeting on Machine Learning (), organised by the GDR REST.