I will present an overview of current challenges, opportunities and strategies in the problem of optimizing density functional theory-based (DFT) simulations using machine learning techniques. I will present three separate but interconnected strategies to expand DFT methodologies into the realm of neural network and modern machine learning techniques. First the traditional force field approach, where neural networks are replacing expensive DFT calculations while promising same accuracy. Secondly I will present our efforts on optimizing the exchange and correlation (XC) functional aiming to improve simultaneously densities and total energies in the Kohn-Sham Hamiltonian. Finally I will introduce our work towards developing a universal electron force field, which aims to achieve multi scaling capabilities while incorporating, at a semi-classical level ions and electrons in the same footing.