Cambridge University - Felix received a B.Sc. and a M.Sc. in physics at Linköping University in Sweden, followed by a Ph.D. in chemistry at the University of Basel. His master’s thesis, which was later published in the International Journal of Quantum Chemistry, was one of the first works that demonstrated successful learning of crystal formation energies. Felix developed machine learning techniques for modeling fundamental quantum mechanical properties. Such properties include energies, forces, and dipole moments of crystals and molecules. In one of his first Ph.D. projects, which was published in Physical Review Letters, Felix used a machine learning model to predict formation energies of almost 2 million Elpasolite crystal structures. This model was also used to identify around 90 potentially thermodynamically stable structures. He was also among those who pioneered the use of quantum mechanical operators directly on machine learning models, which results in improved performance and is a stepping stone towards universal machine learning modeling of quantum mechanical properties. Additionally, Felix dedicated parts of his Ph.D. to benchmarking and comparing the performance of different machine learning models. In one such example Felix and his team, in collaboration with scientists from google, performed one of the most comprehensive comparisons of different machine learning models to date. Felix envisions a research framework where these techniques could be harnessed to understand and model biological and materials systems of varying complexity. Such fundamental models could be used to develop therapeutics, as well as new materials tailored for exhibiting specific properties. To those ends, Felix recently started a postdoctoral fellowship at the University of Cambridge, where applying the models that he developed during his Ph.D. to discover new drugs and materials.
