Solving deep-learning density functional theory via variational autoencoders
In recent years, machine learning models, chiefly deep neural networks, have revealed suited to learn accurate energy-density functionals from data.However, problematic instabilities have been shown to occur in the search of ground-state density profiles via energy minimization.Indeed, any small noise can lead astray from realistic profiles, causin