CECAM flagship workshop: Machine Learning of First Principles Observables

Machine Learning methods have recently found widespread use in areas of atomistic modelling, mainly focusing on developing surrogate models for the potential energy surface with superior computational efficiency while retaining first principles accuracy. However, approaches to learning observable properties directly are just emerging and are challenged by several issues, which we intend to address in the workshop. The event is meant to support the development of a new collaborative, international network connecting different fields of research and integrating the young researchers community with the help of a scientifically diverse, interactive workshop.

Topics: ML of electron density and Hamiltonians, ML of electronic observables, ML of mechanical & magnetic observables, ML of spectroscopic observables, ML of reaction networks, Theoretical and experimental databases